the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Oct 2023
Development and comprehensive analysis of spatially resolved technological high resolution (0.1°×0.1°) Emission Inventory of Particulate Matter for India: A step Towards Air Quality Mitigation
Abstract. Elevated emission of particulate matter (both PM2.5 and PM10) is not limited to urban areas. It’s the major pollutant that drives the air quality across the Indian sub-continent as well as across the globe with adverse health impacts. Moreover, India is home to many polluted cities in recent years that are among the list of most polluting cities in the globe. Therefore, the identification of sources of particulate matter and their quantification along with spatial variability has become of paramount importance from the modelling point of view. The present work is an attempt to develop a high-resolution (~10 km×~10 km) national inventory of particulate (both PM2.5 and PM10) pollutants for India for the base year 2020 using IPCC methodology. The study quantifies the emission load from all possible sources in the county using the best possible resolution activity data and bottom-up approach. The estimated annual emission for PM2.5 and PM10, are calculated to be 15.8 Tg/yr, and 8.3 Tg/yr respectively. The developed emission dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.7885103 (Sahu et al., 2023). Transport-driven windblown road dust remains the dominating source of PM10 emission, while transport and industry are the most important sources of PM2.5. The unattended anthropogenic source - municipal solid waste burning is found to be emerging as a new threat followed by crop residue burning. The developed new surface dataset has formulated a few recommendations of possible mitigation strategies for India and would be a critical tool for modelling studies.
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RC1: 'Comment on essd-2023-310', Anonymous Referee #1, 19 Oct 2023
This manuscript presents a high-resolution PM emission inventory for India 2020 using various bottom-up sources. 15 emission sources of PM are considered which is a large effort. I appreciate the idea and effort of creating such an emission inventory, especially for India. However, I have some concerns about this study and below are some suggestions to improve the quality of the work.
Major comments:
My biggest concern is the methodology. Essential information is missing which makes me unable to evaluate the robustness of results. For example:
- It is unclear how emission sector data are scaled to 0.1*0.1 degree. The authors spent much effort in section 2.1 to introduce all the sources, however, most of the content is background information about each emission sector (e.g. how the sector is important to India or air quality, etc), it is barely (or not at all for many sectors) introduced about the data structure and how the data for each sector are treated. Such information is very important for the data quality of the presented inventory.
- It is unclear how the emission factors and GIS allocation are used. This refers to sections 2.2 and 2.3. The authors wrote that for the emission factors, “The appropriate scientific justification and judgement are discussed and elaborated”, however, I don’t find anywhere with more details. Also, the authors simply referred the method for emission estimate to another publication (Sahu et al., 2023a) without further introduction on the details. For section 2.3, half of the content is about the geography of India (721 districts, 5.89 million km road network, etc), but why they are introduced here? Why are they important to the method (or how relevant to the method)? I don’t find any information on how the authors created 0.1*0.1 degree emission map (is it a modeling approach or a statistical approach?).
- The choice of the year 2020. The authors claimed 2020 as the base year for Indian PM emissions. I’m not convinced about that as 2020 was the first year of COVID and the 2020 emissions are way lower than normal. I recommend the authors extend their work to longer time periods (normally more than 5 years) to meet the interest of the ESSD readers.
- It is unclear about the uncertainty analysis. The authors should clarify in great detail the assessment of uncertainty, as it is important for emission inventory. Currently, the authors only mentioned in section 3.4 that they use Monte Carlo and error propagation.
Other major comments on the results and conclusion:
- Section 3.3 compares this new inventory with other existing inventories. Large discrepancies are shown in many sectors. The authors should explain potential reasons for the difference, is it because of the methodology or model settings or something else? This section deserves more discussion.
- In the conclusion, the authors listed some recommendations for air quality mitigation. Those need to be more specific, such as what the outcomes or benefits would be if that recommendation is achieved (or to some extent). Without such information, I recommend removing the recommendations as they don’t seem to match the other parts of the manuscript.
Additionally, I recommend the authors improve the clarity of the presentation, I have difficulty understanding some of the sentences, and proper references are missing in many statements, below are some examples but not exhaustive:
Line 35: missing reference for ‘~4.2 million people die every year’
Line 60: confusing sentence, what does ‘die a premature death’ mean?
Lines 147-156: need to include version, year and reference for each source, and when cited in the later sections, include the references of these sources.
More examples come in the next section.
Specific comments:
Title: I suggest removing ‘comprehensive’
Line 38: ‘remains as’
Line 48: ‘air pollutant’ should be plural, same for later when it appears.
Line 51: it should be consistent when using abbreviations, the authors should decide to use either the abbreviation “PM” or just “particulate matter” throughout the entire manuscript, but not mix them.
Line 58: remove ‘your’
Line 62: why and how ‘chronologically’?
Line 81: ‘because of’ to ‘from’
Line 81: ‘it has’ to ‘they have’
Line 94: for ‘Sahu et al., 2021’, there are actually two Sahu et al., 2021 in the reference list, which one?
Lines 158-162: need some references
Line 167: classified by what feature?
Line 171: ‘plying’?
Figure 1: (1) for (b), I suggest plotting it by the order of contribution, i.e. Two wheeler (what is the percentage?), then next to 11.05%, then 4.51%, etc. (2) add (a)(b)(c) in the caption
Line 545: references for ‘widely used in emission inventories’
Citation: https://doi.org/10.5194/essd-2023-310-RC1 -
AC1: 'Reply on RC1', Saroj Kumar Sahu, 05 Nov 2023
This manuscript presents a high-resolution PM emission inventory for India 2020 using various bottom-up sources. 15 emission sources of PM are considered which a large effort is. I appreciate the idea and effort of creating such an emission inventory, especially for India. However, I have some concerns about this study and below are some suggestions to improve the quality of the work.
Major comments:
My biggest concern is the methodology. Essential information is missing which makes me unable to evaluate the robustness of results. For example:
It is unclear how emission sector data are scaled to 0.1*0.1 degree. The authors spent much effort in section 2.1 to introduce all the sources, however, most of the content is background information about each emission sector (e.g. how the sector is important to India or air quality, etc), it is barely (or not at all for many sectors) introduced about the data structure and how the data for each sector are treated. Such information is very important for the data quality of the presented inventory.
Response: We appreciate the referee's insightful comment. We have taken into account those sources which are traditionally dominating in Indian geographic regions followed by sources that are driven by local areas.
There are many such unattended sources, which have never been quantified accurately, that are being considered for present emission estimation and are elaborated in section 2.1. The spatial information of activity and associated calculated emission using IPCC tier-2 based bottom-up approach considered for the first time in our emission. The activity data collected via various authentic govt. sources are cross-checked for quality and accuracy. For example: In the case of power plants, information like the exact location, capacity of power plant, technology use, fuel type and quality are validated with various government reports/third-party (paid) data sources.
Regarding discussing source-specific activity data, it is critical to address this information (data) in depth due to space limitations. These activity data serve as the foundation for calculating (quantifying) the emission. As a result, they are critical in understanding the gravity of the crisis on a national scale. Nonetheless, in Section 3.1., i.e. GIS-based spatial information of each kind of sectoral emissions, we have scaled sector-specific emission data to the final 0.10×0.10 degree.
Since emission and inventory processes might have errors due to the reporting of data by third parties, primary sources etc. There is a chance of error in reporting data for various sectors. We do a thorough quality check to avoid errors. The quality and accuracy of emission inventory and associated errors are always reported by uncertainty, which is performed in the present study too. We have calculated the uncertainty based on used activity data and EFs used and reported in section 3.4.
It is unclear how the emission factors and GIS allocation are used. This refers to sections 2.2 and 2.3. The authors wrote that for the emission factors, “The appropriate scientific justification and judgement are discussed and elaborated”, however, I don’t find anywhere with more details. Also, the authors simply referred the method for emission estimate to another publication (Sahu et al., 2023a) without further introduction on the details. For section 2.3, half of the content is about the geography of India (721 districts, 5.89 million km road network, etc), but why they are introduced here? Why are they important to the method (or how relevant to the method)? I don’t find any information on how the authors created 0.1*0.1 degree emission map (is it a modeling approach or a statistical approach?).
Response: Thank you for your comment. Sections 2.2 and 2.3 state the emission factors and methodology that have been used to develop the national emission inventory. IPCC tier-2 approach is widely being used for reliable emission estimation where the emission is calculated for each source location based on fuel activity data and corresponding country-specific technological EFs. This bottom-up approach uses sector-specific technological emission factors and has historically been widely used across the globe to improve emission accuracy and uncertainty. We have reported this approach in detail in a previous couple of works of ours; hence, to avoid repetition we have provided recent references, where the same country-specific technological emission factors are being adopted to develop emission inventories for other Indian megacities. If the referee wishes then we can submit the EFs database as supplementary material.
Sahu et al, 2023 in ESSD, our recent paper has adopted similar EFs and methodology for megacity Delhi. To not repeat it, we have cited the paper for detailed EFs and methodology. If the reviewer wishes to add then we will add it again. Since the estimation of gridded emission includes such detailed proxy information we describe it in section 2.3. Now we have removed this information to the activity section under proxy data used.
The gridded (i.e. 0.10*0.10 degree) emission map is created based on a statistical approach where the source-specific (say transport, industry, residential cooking, MSW, Power etc.) emission layers are prepared based on spatial information of fuel activities and corresponding calculated emission. To prepare such a layer of information from various emission sources, the geospatial data includes information about road networks, geographic areas, population, and much more play a vital role. The generated, all of which is correlated with precise geographical places on the Earth's surface. These data serve as the foundation for allocating emissions spatially. Section 3.1. GIS-based spatial allocation of emission: contains every detail of how the authors created a 0.1×0.1 degree emission map.
The choice of the year 2020. The authors claimed 2020 as the base year for Indian PM emission. I’m not convinced about that as 2020 was the first year of COVID and the 2020 emissions are way lower than normal. I recommend the authors extend their work to longer time periods (normally more than 5 years) to meet the interest of the ESSD readers.
Response: To be clear, our data is generated for the period of 'April 2019 to March 2020' and considered as the base year 2020 which is purely without the COVID-19 pandemic and scenarios of a state-wide lockdown. Given that the lockdown in India started around the end of March 2020 and that emissions are not expected to change significantly. We are hopeful that the most recent estimates can accurately reflect post-pandemic possibilities. For this reason, we have set 2020 as the base year for current emissions, as stated in the manuscript's introduction. Although we welcome the recommendations to extend the period, it should be noted that developing emission inventories even for a year is a very complex process that takes a significant amount of time and effort. Definitely time series could have been intriguing to investigate the trend of emissions in countries like India, is not our focus due to the limitation of resources.
It is unclear about the uncertainty analysis. The authors should clarify in great detail the assessment of uncertainty, as it is important for emission inventory. Currently, the authors only mentioned in section 3.4 that they use Monte Carlo and error propagation.
Response: Thank you for a genuine comment; we have followed both the linear error propagation method and the Monte Carlo simulation methodology for the uncertainty estimation as recommended by IPCC.
In the linear error propagation method, individual source-specific percentage uncertainty of activity data (UAD) and the emission factor (UEF) are used. The Source-specific combined uncertainties (UC) are calculated using the formula –
The overall uncertainty (UI) of the inventory is calculated using the source-specific Emission (E) and the combined uncertainties (Uc).
In the Monte Carlo Simulation method, the source-specific activity data and the emission factors data are plotted and fitted to the five probability distribution functions viz. Normal distribution, Log-Normal Distribution, Student’s t-distribution, Triangular distribution and Uniform distribution. The output of the sector-specific uncertainties is calculated using the known function of each distribution. Every sectoral uncertainty output is iterated 100000 times and finally the mean, Standard deviation and 95% confidence interval is calculated. All the necessary statistical calculations are done in the IBM SPSS 24.0 (Paliwal et al., 2016).
Other major comments on the results and conclusion:
Section 3.3 compares this new inventory with other existing inventories. Large discrepancies are shown in many sectors. The authors should explain potential reasons for the difference, is it because of the methodology or model settings or something else? This section deserves more discussion.
Response: This is an expected comment and I must thank the reviewer. After a complete and thorough review of all past research works, it is observed that the large discrepancies are due to varying sectors included in different studies, fuel activity used, methodology used, adopted country-specific technological emission factors and varying base year. The activity data used for a couple of sectors has varying resolutions. Since the reported earlier studies have limited information on activity data, it is very difficult to compare directly. That is the reason the quality and accuracy of reported emission inventories are judged based on uncertainty, which is typically driven by emission factors and activity data used. We have addressed the issue in Section 3.3. In our comparison studies, we have highlighted the limitations and causes of discrepancy.
In the conclusion, the authors listed some recommendations for air quality mitigation. Those need to be more specific, such as what the outcomes or benefits would be if that recommendation is achieved (or to some extent). Without such information, I recommend removing the recommendations as they don’t seem to match the other parts of the manuscript.
Response: I agree with the reviewer, keeping the ongoing air quality issues in India, we believe the sectors responsible for such high particulate emission load are being quantified in the present study, which could be a very essential piece of information/data to policymakers. Indian cities are experiencing high particulate matter load and are among the list of most polluting cities across the world. Bringing focus to potential mitigating initiatives that could enhance the impact of developed data use for policymakers in India as well as for scientific communities across the globe. We've emphasized the source-specific mitigation strategy that can be implemented if steps are taken. As per suggestion, we will add more specific vehicle types and age-wise categories responsible for high emissions. Similarly, we will include more precise information for other sectors like Industry and residue.
Additionally, I recommend the authors improve the clarity of the presentation, I have difficulty understanding some of the sentences, and proper references are missing in many statements, below are some examples but not exhaustive:
Line 35: missing reference for ‘~4.2 million people die every year’
– WHO, 2022. We shall add the reference in manuscript too.
Line 60: confusing sentence, what does ‘die a premature death’ mean?
- The phrase “die a premature death” means someone dies at a younger age than expected or in an untimely manner effectively here causes of premature death include disease e.g. cancer, heart disease due to air pollution. We have changed it to “die prematurely” for more clarity. We recheck the manuscript for any such kind of mistake.
Lines 147-156: need to include version, year and reference for each source, and when cited in the later sections, include the references of these sources.
– As each source is discussed separately, the sectorial reference details are given. We have only stated the names in full in the aforementioned Line no. s in order to use them subsequently in an abridged form.
More examples come in the next section.
Specific comments:
Title: I suggest removing ‘comprehensive’
-As per suggestion we will remove “Comprehensive” from the title.
Line 38: ‘remains as’
-As per remark, It is replaced with “is”
Line 48: ‘air pollutant’ should be plural, same for later when it appears.
-We have corrected and replaced in all places in the manuscript.
Line 51: it should be consistent when using abbreviations, the authors should decide to use either the abbreviation “PM” or just “particulate matter” throughout the entire manuscript, but not mix them.
-As per suggestion, we have checked and corrected.
Line 58: remove ‘your’
-It is removed.
Line 62: why and how ‘chronologically’?
-It is removed from the sentence as just two studies are cited.
Line 81: ‘because of’ to ‘from’
-Replaced in the sentence.
Line 81: ‘it has’ to ‘they have’
-Replaced in the sentence.
Line 94: for ‘Sahu et al., 2021’, there are actually two Sahu et al., 2021 in the reference list, which one?
-This is supposed to be Sahu et al, 2021b and corrected in manuscript.
Lines 158-162: need some references
-Added to manuscript now
Line 167: classified by what feature?
-It is classified based on size, usage type, and technology. Now the sentence is being rewritten.
Line 171: ‘plying’?
“Plying” is run on road but for clarity, we have replaced with “running”
Figure 1: (1) for (b), I suggest plotting it by the order of contribution, i.e. Two wheeler (what is the percentage?), then next to 11.05%, then 4.51%, etc. (2) add (a)(b)(c) in the caption
-As per suggestion and remark, we have rectified in the manuscript.
Line 545: references for ‘widely used in emission inventories’
-Couple of references are added now
As per reviewer’s valuable suggestions, we have tried our best to incorporate in the manuscript.
Citation: https://doi.org/10.5194/essd-2023-310-AC1
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RC2: 'Comment on essd-2023-310', Anonymous Referee #2, 08 Nov 2023
Review for manuscript: ‘Development and comprehensive analysis of spatially resolved technological high resolution (0.1°×0.1°) Emission Inventory of Particulate Matter for India: A step Towards Air Quality Mitigation’
This publication deals with the development of a high resolution emission inventory for particulate matter (PM10 and PM2.5) over India for the year 2020. The work is very interesting since it deals with a highly polluted area in the world, thus having significant health related implications.
However, the manuscript requires major revisions in terms of clarity, methodological details and results presentation.
General comments:
-the methodology for emission estimation and the data sources used for each sector should be clarified. A Table could help in summarizing all the data sources.
-the methodology for producing spatially distributed emissions is not completely clear: do the authors apply a downscaling procedure of national emissions over the global gridmap at 10x10 km resolution? A Table could help to summarise approaches and data used for producing emission gridmaps.
-The authors should clarify the sector definition linking each sector to the corresponding IPCC categories (it can be done also in a supplementary table).
-the authors should explain why this work should be considered as a reference emission inventory for India or why it represents an improvement compared to existing inventories for India (e.g. REAS and others). Statistics used for estimating the emissions may not be complete and uncertain also in this work. Comments from the authors may provide indications on the limitations of this work.
-Why the authors provide estimates only on PM10 and PM2.5 and not on BC and OC?
-In order to run air quality models, all air pollutant emissions are needed over the domain of interest. How can regional air quality modellers use this work when only PM emissions are provided? Recommendations on which dataset could complement the current work for all other pollutants should be provided.
-the manuscript contains several English mistakes, typos, spaces missing between words etc. Accurate revision of the text is required before publication.
Detailed comments
-line 21: IPCC does not provide a methodology for estimating air pollutant (PM) emissions. Please clarify what is exactly meant here.
-the structure and story flow of the introduction is confusing and should be revised. It starts with a global picture, then it moves to India, then to South Asia…then it addresses again the health effects of PM and finally again back to India (although not explicitly mentioned).
-line 85: what is meant with ultra-precision?
-line 111: countries (e.g. in the world) or country (i.e. India)?
-lines 115-116: what type of sources are different from urban and rural sites? Is it mostly the relative share of the emissions being different between the 2 sites or are the sources different?
-line 125: remove major/minor.
-line 127 and 136 (and everywhere in the text when it appears): remove ‘based’ before EF
-line 136: what are secondary activity data? What is meant with secondary emissions?
-line 173: MT should be Mt and similarly everywhere in the text.
-line 220: replace decides with determines.
-line 303: do waste emissions belong to household or waste sector? Why are they included in households? Again linking to IPCC sectors would be useful.
-line 318: street vendor represents a specific activity for India and certain countries. However, it could be possibly related with emissions from combustion in commercial activities and services from sector 1A4 of IPCC. Is it correct?
-lines 342-344: not clear what was done to deal with missing/uncertain information.
-line 413 Kg should be kg. This correction should be applied everywhere in the text.
-line 428: it should read ‘and therefore waste is burned..’
-line 447: this paragraph discusses construction activities. What is the data source of the statistics used in the emission computation?
-Figure 2 presents several features of the Indian territory with maps. I think not all of them are relevant or discussed in the current work. I suggest including in the main text only those maps relevant for the emission work (e.g. removing water bodies, since no shipping emissions are discussed, etc.). The authors could split the figure to have first the characterization of the Indian territory, then the degree of urbanization and hen a focus on point sources, linear sources etc.
-line 537: actually no methodology and scientific justifications are presented. The authors should add a section on how emissions where calculated, data sources used, EFs values, and clarify any assumption made for each sector.
-section 2.3: it is not clear how spatially distributed emissions were calculated. Are they the result of the downscaling of a national total through the use of spatial proxies?
-section 3.3: in order to facilitate the comparison among the different inventories, I suggest including a Table summarizing for each sector the emission values provided by each inventory. REAS is used in the HTAP_v3 emission mosaic (https://essd.copernicus.org/articles/15/2667/2023/) as reference inventory for India. Why your study differs so much from REAS? A more consistent discussion should be provided in this section, using IPCC sectors to aggregate the emissions from different inventories. What is the reference year for your comparison? 2020 for all inventories? Figure 6 is not always clear, for example in the top panel power plant emissions for the current work are not visible. I suggest improving the visualization of these results.
-section 3.4 line 689: it is not clear from where uncertainty values for AD and EF are taken. References for uncertainty estimates should be provided.
-line 721: what is the contribution of super-emitting vehicles to PM emissions in India? I think adding a section in the transport sector description regarding super-emitters would be valuable.
-the conclusion section lists several mitigation measures. What is the feasibility of each of them? For example, how fuel adulteration could be monitored (lines 738-739)? Point b) is not clear. I suggest expanding this section including more details for each mitigation option.
-line 732: What is the usage of improvised public transport system? Do you mean ‘improved’?
-line 823 Crippa et al. 2019 refers to a paper on temporal profiles which are not discussed in the current work. If the authors are searching for a reference for the EDGAR inventory, they should mention the version used and include the corresponding citation (e.g. doi:10.1016/j.enpol.2022.113021, doi:10.5194/essd-10-1987-2018, https://edgar.jrc.ec.europa.eu/dataset_ap61).
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AC2: 'Reply on RC2', Saroj Kumar Sahu, 18 Nov 2023
- Thank you for the suggestion. The sources of the activity data have been listed next to each sector; however, in accordance with the reviewer's recommendation, we will compile the sources into a table and offer them as supplemental material to prevent repetition in the text. Since the methodology is an IPCC-defined bottom-up based standard method which is being traditionally used in developing emissions with minimum uncertainty, so we have elaborated further.
- Thank you for your remark. In order to improve the clarity, we have included more descriptions in explaining the proxy and digital data used for each sector like road network as proxy in transport sectors, forest cover, agriculture land, village level population and its political boundary. We haven't downscaled our national emissions from global gridmaps because doing so entails uncertainty in regional database emissions. In order to maintain the originality of the Indian emissions we have tried to develop the gridded (i.e. 0.1×0.1 degree) emission map using a GIS-based statistical approach in which source-specific emission layers (for example, transportation, industrial, residential cooking, MSW, Power, and so on) are prepared where the spatial information of each activity and related computed emissions is organized as a thematic layer. Geospatial data, which contains information about Indian road networks, geographic areas, population, and much more, is critical in preparing such a layer of information from diverse emission sources. The generated data with exact spatial location is key and foundation for spatially assigning emissions for each sector.
- As per the suggestion, we have provided the link to sectoral data used in the present calculation. The tabulated information is also attached as supplementary data (Table-1S).
Sl. No.
IPCC 2006 code
Sector
Source
1
1.A.3
Transport
Ministry of Road Transport & Highway
https://morth.nic.in/
Ministry of Statistics and Programme Implementation
https://www.mospi.gov.in/
2
EPA AP-42*
Wind-blown Road Dust
Ministry of Road Transport & Highway
https://morth.nic.in/
Indian Metrological Department’s (IMD)
https://mausam.imd.gov.in/
3
1.A.2
Industry
Ministry of Petroleum and Natural Gas
https://petroleum.nic.in/
Ministry of Micro, Small & Medium Enterprises
https://msme.gov.in/
Ministry of Statistics and Programme Implementation
https://www.mospi.gov.in/
4
1.A.1
Thermal Power Plant
Ministry of Power
https://powermin.gov.in/
Central Electricity Authority
https://cea.nic.in/?lang=en
5
1.A.4.b
Residential and Slum
Census of India
https://censusindia.gov.in/census.website/
Ministry of Housing and Urban Affairs
https://mohua.gov.in/
UN World Urbanization Prospects
https://population.un.org/wup/
7
1.A.4.a
Street Vendor
India’s Street Vending (Protection of Livelihoodand Regulation of Street Vending) Act https://mohua.gov.in/upload/uploadfiles/files/StreetVendorAct2014
8
3.C.1.b
Crop Residue Burning
Ministry of Agriculture & Farmers' Welfare
https://agricoop.gov.in/
Ministry of Statistics and Programme Implementation
https://www.mospi.gov.in/
9
1.B.1
Crematorium
Ministry of Home Affairs
https://www.mha.gov.in/en
SAFAR- Delhi (2018), Pune (2020)
10
Diesel Generator
International Energy Agency
https://www.iea.org/
Department of Telecommunication
https://dot.gov.in/
11
4.C.2
Municipal Solid Waste
Central Pollution Control Board
https://cpcb.nic.in/
12
4.C.1
Municipal Solid Waste Incineration Plant
Central Pollution Control Board
https://cpcb.nic.in/
13
1.B.1
Brick Kiln
Central Pollution Control Board
https://cpcb.nic.in/
Seay et al., (2021)
https://doi.org/10.1088/2515-7620/ac0a66
Rajarathnam et al., (2014)
https://doi.org/10.1016/j.atmosenv.2014.08.075
14
Cow Dung (Biofuel)
SAFAR- Delhi (2018), Pune (2020)
16
2.B.9.b
Incense stick/ Mosquito coil/ Cigarette
Cohen et al., (2013)
https://doi.org/10.1016/j.scitotenv.2013.03.101
Kumar et al., (2014)
http://dx.doi.org/10.4103/0972-6691.140770
17
1.A.2.k
Construction Activity
Central Pollution Control Board
https://cpcb.nic.in/
*US EPA AP-42 Section 13.2
- This is an expected comment and I must thank the reviewer for giving us a chance to explain. In the present inventory, we have considered spatial information of all activities under various sectors responsible for emission of particulate matter using an IPCC tier-2/3 based bottom-up technique, where country specific technological emission factors and resolution of the activity data is vital. The present approach not only improves the spatial information of emission hotspots due to inclusion of high-resolution proxy data and activity data but also reduces the uncertainty. Bottom-up emission inventories method entails gathering comprehensive information about sources and often rely on data at the source level, such as fuel usage, manufacturing processes, and activity levels like industrial type, capacity, production, fuel quantity used, vehicular type, technology used, age of vehicles, road condition (paved or unpaved), silt load in road, moisture pattern, cooking fuel types, aviation fuel, residue generated and burning pattern across the country, thermal power plants, waste burning and waste to energy generation etc., to calculate emission at source/grid level (i.e. bottom-up). This extensive information enables a thorough and sector-specific examination of emissions. It can, however, be resource-intensive and may necessitate significant data collection activities. Whereas in top-down approach estimation of emissions for a country is done at coarse level and is distributed based on few major proxies like population, urbanization and mostly land use pattern. This approach is adopted where the access to find resolution activity data is limited.
Unlike present emission inventory, REAS Emission Inventory is based on the IPCC-defined methodology where many sectoral emissions are calculated using top-down approach due to limitation of data. Moreover, many emission factors adopted are not country specific due to poor understanding of regional sources. Furthermore, the activity data for various sectors adopted from the international database rather than India-specific data, resulting in increased uncertainty. Since uncertainty is common in any inventory development due to inclusion of secondary sources, based activity data and emission factors adopted. Based on the best judgement and comparison, present inventory has less uncertainty. However, bottom-up emission inventories are widely being used by researchers, policymakers, and environmental agencies to examine the sources of emissions, identify areas for improvement, and devise strategies for minimizing environmental impacts. These inventories are critical in the development and assessment of policies aimed at controlling emissions and improving air quality.
- In India, PM is considered as a major pollutant responsible for changing air quality in many cities/megacities and modulating parameters in defining the Air Quality Index (AQI). In terms of health impact, both pollutants have significant impact and their sources are linked to similar kinds of sources in India. Development of emission inventory requires pollutant-wise country/sectoral specific technological EFs. In the case of PM10 and PM5, we have prepared a comprehensive database of all EFs through our scientific understanding and available data. Keeping the limitation of BC and OC in few major sectors (say limitation of OC EFs for transport sectors and diesel use in various sectors), we have not considered BC/OC along with gaseous pollutants in the present study but we are in process of reporting all other pollutants in our next manuscript. As a result, PM10 and PM2.5 remained our primary pollutant of interest to address the air quality issues in the country.
- I agree with the reviewer’s remark. Particulate matter (PM10, PM5) is the major pollutant in the Indian context. The developed high-resolution gridded dataset can be used along with other available global inventories like EDGAR/REAS etc. over India. Moreover, we are working diligently to develop the emission database for all other pollutants and will communicate very soon.
- We are sorry for the errors. We shall thoroughly look into these and make all corrections.
- I agree with the reviewer, the IPCC do not provide any specific methodology for pollutant specific estimation but approach to adopting a top-down or bottom-up approach for the development of the Emission Inventory is standard. Depending on the availability of detailed sector specific activity data (fine & coarse resolution) for a particular country, the approach is adopted to estimate emission. The IPCC report shows how to carry out the estimation process as per the proxy data availability, by differentiating it into Tier I, II, and III. We have adopted the IPCC-defined bottom-up approach for emission estimation based on Tier II and III data/EFs.
- We take the reviewer's suggestions into account. We will make necessary changes to the introduction section while preserving its proper flow and avoiding any detriment to the intended message.
- By ‘ultra-precision’ we tried to mean the fine resolution (0.1×0.1 degree) and spatially resolved emission hotspots. However, for clarity, we have replaced it with “spatially resolved” and rewrote the sentence.
- considering the remark, we will replace it to the country (India)
- India, as a developing country, has a both rural and urban population in its territory, with rural covering the larger proportion. When it comes to rural and urban areas, the types of sources do differ. The sources like transportation, industrial activity, and street vendors are more prominent in the urban area, whereas solid and biofuel usage in residential cooking activity, brick kilns, crop residue burning, mosquito coil burning etc. are the predominant sources of emission in the rural areas. So the share of emissions changes.
- Thank you for the remark. We shall consider the change.
- Thank you for the comment, we will remove the same throughout the manuscript.
- Secondary activity data is simply information obtained from a government sources and paid statistic data sites like indiastat etc. It has absolutely nothing to do with secondary emissions. We apologize for using a potentially confusing term. We shall modify the sentence in order to reflect its exact meaning.
- Thank you for the remark. We will make the corrections everywhere in the manuscript.
- Thank you for the remark. We shall consider the change.
- In response to the comment, we'd like to highlight a few key points. The waste sector of India deals with the waste generated especially from households as a major part as the population exceeds ~1.3 billion and is dumped at landfill sites. Since the major contributor of waste is household based activities and estimated accordingly. The gross solid waste generated from each household and applied to the waste sector as a whole. We have assigned the IPCC defined sector code 4.C.1 & 4.C.2 to this sector in a separate table.
- I agree with the reviewer’s remark. The Street vendor is related to the emission from commercial combustion activity similar to the IPCC 2006 code 1.A.4.a.
- In order to improve the missing/uncertainty information, data generated through our previous emission inventory campaigns were used to fill the gap and to improve the understanding about sources like data regarding the type of fuel used for commercial cooking and its consumption pattern by street vendors.
- Thank you for the remark. We will make the corrections everywhere in the manuscript.
- Thank you for the remark. We made the changes in manuscript.
- The report of Central Pollution Control Board - Guidelines on Environmental Management of Construction & Demolition (C & D) Wastes 2019-20 is the source. We shall add in the manuscript as well.
- Water bodies were used mask out the areas where the emission are not likely to happen. This one of the important component of land Use land cover pattern and are used indirectly improve the spatial emission. If reviewer wish to remove it then we will remove it.
- Our recent manuscript i.e. Sahu et al, 2023 in ESSD, has adopted similar EFs and methodology where the emissions were estimated at 400mt resolution for National capital megacity “Delhi”. To not repeat it, we have cited the paper for detailed EFs and methodology. If the reviewer wishes to add then we will add it again.
- Thank you for your concern regarding Section 2.3, which states about the emission factors and methodology that has been used to develop the national emission inventory. IPCC tier-2/3 approach is widely being used for reliable emission estimation where the emission is calculated for each source location based on fuel activity data and corresponding country specific technological EFs. This bottom-up approach uses sector specific technological emission factors, and has historically been widely being used across the globe to improve the emission accuracy and uncertainty. We have reported this approach in detail in the previous couple of works of ours; hence, to avoid repetition we have provided recent references, where the same country specific technological emission factors are being adopted to develop city emission for other Indian megacities. If the referee wishes then we can submit the EFs database as supplementary material.
As mentioned earlier, Sahu et al, 2023 in ESSD, our recent paper has adopted similar EFs and methodology for megacity Delhi. In order to not repeat it again, we have cited the paper for detailed EFs and methodology. If the reviewer wishes to add then we will add it again. Since the estimation of gridded emission includes such detailed proxy information so we describe it in section 2.3. Now we have removed this information to the activity section under proxy data used.
The gridded (i.e. 0.1*0.1 degree) emission map is created based on a statistical approach where the source specific (say transport, industry, residential cooking, MSW, Power etc.) emission layers are prepared based on spatial information of fuel activities and corresponding calculated emission. To prepare such a layer of information from various emission sources, the geospatial proxy data includes information about road networks, location of industries, forest/agriculture land cover, geographic areas, population, and much more play a vital role. The generated dataset is correlated with precise geographical places on the Earth's surface. These data serve as the foundation for allocating emissions spatially. Section 3.1. GIS based spatial allocation of emission: contains every detail of how the authors created 0.1*0.1 degree emission map.
- We thank the referee for suggestions. We will put the values in a table and also provide revised plot (Figure 6). Moreover, major sectors in REAS inventory are based on the IPCC-defined Top-down approach for emission estimation. Moreover, the activity data for various sectors adopted from the international database rather than India-specific data, resulting in increased uncertainty. Since each inventory is developed for a different base year with different sets of activity data and emission factors, there is a large disparity in reported emission by various research communities. Present inventory is estimated for 2020, which is the most recent year where country specific technological EFs are adopted for 17 sectors which is first of its kind emission dataset reported for India. In our case, base year 2020 includes data from (April 2019 to March 2020) and as there is no significant shift in emission this will accurately reflect post-pandemic emission scenarios, as stated in the manuscript's introduction section.
- As per suggestion it is provided in the manuscript. We used both the linear error propagation approach and the Monte Carlo simulation methodology for estimating uncertainty, as recommended by the IPCC and widely used by emission inventory research communities. Both Individual source-specific percentage uncertainty of activity data (UAD) and the emission factor (UEF) is adopted. The Source-specific combined uncertainties (UC) are calculated using the formula -
The overall uncertainty (UI) of the inventory is calculated using the source-specific Emission (E) and the combined uncertainties (Uc).
In the Monte Carlo Simulation method, the source-specific activity data and the emission factors data are plotted and fitted to the five probability distribution functions viz. Normal distribution, Log-Normal Distribution, Student’s t-distribution, Triangular distribution and Uniform distribution. The output of the sector-specific uncertainties is calculated using the known function of each distribution. Every sectoral uncertainty output is iterated 100000 times and finally the mean, Standard deviation and 95% confidence interval is calculated. All the necessary statistical calculations are done in the IBM SPSS 24.0 (Paliwal et al., 2016).
- Vehicles older than 15 years old (super-emitters) account for about 42% of total vehicular PM10 emissions. We will include a paragraph in this context in the document, as suggested by the reviewer.
- This is a very good suggestion. We appreciate the reviewer’s remark. However, the editor of ESSD suggests that this is a dataset paper and the mitigation measures do not comply with the theme of the manuscript. They want it to be removed. So, we are removing the whole mitigation section from the text.
- Yes, a seamless and upgraded (improved) public transportation system is critical in India in order to limit the use of private automobiles and encourage the people to drive using public transportation. Here improved means of public transport with new fleets with cleaner fuel and better emission norms, which will lower vehicular emissions to some extent. We shall replace the term ‘improvised’ to ‘improved’.
- Thank you very much for your genuine comment. We will replace the reference with the suggested one and use the version now.
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AC2: 'Reply on RC2', Saroj Kumar Sahu, 18 Nov 2023
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EC1: 'Comment on essd-2023-310', Tobias Gerken, 12 Nov 2023
We have now received two referee reports which are generally supportive of the idea and the work conducted. I would like to thank the reviewers for their careful consideration of this manuscript.
Both referee reports highlight the need to better communicate the underlying methodology for calculating the emission dataset and to improve the clarity of the manuscript. I recommend that the authors carefully consider the referee comments when preparing their response and revised submission. In line with the authors' desire to provide a basis for mitigating air quality issues, it is key that potential users and the scientific community at large have a complete understanding on the creation of this dataset including data sources and their processing. Ideally this would include the software/ code that is used to generate the dataset.
Additionally, because this is manuscript submitted to a data journal introducing a new data set, I would strongly suggest removing recommendations to improve air quality, which seem out of place in this context and should be more carefully backed up by scientific results outside of a data paper.
Citation: https://doi.org/10.5194/essd-2023-310-EC1 -
AC3: 'Reply on EC1', Saroj Kumar Sahu, 18 Nov 2023
We are thankful that both referees are supportive of our work. As per suggestion, we have tried our level best to make necessary changes in the manuscript so that methodology and data used should be more transparent. I welcome the Editor’s suggestion to remove the mitigation section and we have removed it from the manuscript. The whole emission inventory process does not follow any software code but it is a statistical approach to process the sectoral emission estimation in the GIS environment and converting into desired format as per need. Here we have prepared gridded emissions for further use in any kind of model. The line numbers (735-767) is removed from the manuscript.
Citation: https://doi.org/10.5194/essd-2023-310-AC3
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AC3: 'Reply on EC1', Saroj Kumar Sahu, 18 Nov 2023
Status: closed
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RC1: 'Comment on essd-2023-310', Anonymous Referee #1, 19 Oct 2023
This manuscript presents a high-resolution PM emission inventory for India 2020 using various bottom-up sources. 15 emission sources of PM are considered which is a large effort. I appreciate the idea and effort of creating such an emission inventory, especially for India. However, I have some concerns about this study and below are some suggestions to improve the quality of the work.
Major comments:
My biggest concern is the methodology. Essential information is missing which makes me unable to evaluate the robustness of results. For example:
- It is unclear how emission sector data are scaled to 0.1*0.1 degree. The authors spent much effort in section 2.1 to introduce all the sources, however, most of the content is background information about each emission sector (e.g. how the sector is important to India or air quality, etc), it is barely (or not at all for many sectors) introduced about the data structure and how the data for each sector are treated. Such information is very important for the data quality of the presented inventory.
- It is unclear how the emission factors and GIS allocation are used. This refers to sections 2.2 and 2.3. The authors wrote that for the emission factors, “The appropriate scientific justification and judgement are discussed and elaborated”, however, I don’t find anywhere with more details. Also, the authors simply referred the method for emission estimate to another publication (Sahu et al., 2023a) without further introduction on the details. For section 2.3, half of the content is about the geography of India (721 districts, 5.89 million km road network, etc), but why they are introduced here? Why are they important to the method (or how relevant to the method)? I don’t find any information on how the authors created 0.1*0.1 degree emission map (is it a modeling approach or a statistical approach?).
- The choice of the year 2020. The authors claimed 2020 as the base year for Indian PM emissions. I’m not convinced about that as 2020 was the first year of COVID and the 2020 emissions are way lower than normal. I recommend the authors extend their work to longer time periods (normally more than 5 years) to meet the interest of the ESSD readers.
- It is unclear about the uncertainty analysis. The authors should clarify in great detail the assessment of uncertainty, as it is important for emission inventory. Currently, the authors only mentioned in section 3.4 that they use Monte Carlo and error propagation.
Other major comments on the results and conclusion:
- Section 3.3 compares this new inventory with other existing inventories. Large discrepancies are shown in many sectors. The authors should explain potential reasons for the difference, is it because of the methodology or model settings or something else? This section deserves more discussion.
- In the conclusion, the authors listed some recommendations for air quality mitigation. Those need to be more specific, such as what the outcomes or benefits would be if that recommendation is achieved (or to some extent). Without such information, I recommend removing the recommendations as they don’t seem to match the other parts of the manuscript.
Additionally, I recommend the authors improve the clarity of the presentation, I have difficulty understanding some of the sentences, and proper references are missing in many statements, below are some examples but not exhaustive:
Line 35: missing reference for ‘~4.2 million people die every year’
Line 60: confusing sentence, what does ‘die a premature death’ mean?
Lines 147-156: need to include version, year and reference for each source, and when cited in the later sections, include the references of these sources.
More examples come in the next section.
Specific comments:
Title: I suggest removing ‘comprehensive’
Line 38: ‘remains as’
Line 48: ‘air pollutant’ should be plural, same for later when it appears.
Line 51: it should be consistent when using abbreviations, the authors should decide to use either the abbreviation “PM” or just “particulate matter” throughout the entire manuscript, but not mix them.
Line 58: remove ‘your’
Line 62: why and how ‘chronologically’?
Line 81: ‘because of’ to ‘from’
Line 81: ‘it has’ to ‘they have’
Line 94: for ‘Sahu et al., 2021’, there are actually two Sahu et al., 2021 in the reference list, which one?
Lines 158-162: need some references
Line 167: classified by what feature?
Line 171: ‘plying’?
Figure 1: (1) for (b), I suggest plotting it by the order of contribution, i.e. Two wheeler (what is the percentage?), then next to 11.05%, then 4.51%, etc. (2) add (a)(b)(c) in the caption
Line 545: references for ‘widely used in emission inventories’
Citation: https://doi.org/10.5194/essd-2023-310-RC1 -
AC1: 'Reply on RC1', Saroj Kumar Sahu, 05 Nov 2023
This manuscript presents a high-resolution PM emission inventory for India 2020 using various bottom-up sources. 15 emission sources of PM are considered which a large effort is. I appreciate the idea and effort of creating such an emission inventory, especially for India. However, I have some concerns about this study and below are some suggestions to improve the quality of the work.
Major comments:
My biggest concern is the methodology. Essential information is missing which makes me unable to evaluate the robustness of results. For example:
It is unclear how emission sector data are scaled to 0.1*0.1 degree. The authors spent much effort in section 2.1 to introduce all the sources, however, most of the content is background information about each emission sector (e.g. how the sector is important to India or air quality, etc), it is barely (or not at all for many sectors) introduced about the data structure and how the data for each sector are treated. Such information is very important for the data quality of the presented inventory.
Response: We appreciate the referee's insightful comment. We have taken into account those sources which are traditionally dominating in Indian geographic regions followed by sources that are driven by local areas.
There are many such unattended sources, which have never been quantified accurately, that are being considered for present emission estimation and are elaborated in section 2.1. The spatial information of activity and associated calculated emission using IPCC tier-2 based bottom-up approach considered for the first time in our emission. The activity data collected via various authentic govt. sources are cross-checked for quality and accuracy. For example: In the case of power plants, information like the exact location, capacity of power plant, technology use, fuel type and quality are validated with various government reports/third-party (paid) data sources.
Regarding discussing source-specific activity data, it is critical to address this information (data) in depth due to space limitations. These activity data serve as the foundation for calculating (quantifying) the emission. As a result, they are critical in understanding the gravity of the crisis on a national scale. Nonetheless, in Section 3.1., i.e. GIS-based spatial information of each kind of sectoral emissions, we have scaled sector-specific emission data to the final 0.10×0.10 degree.
Since emission and inventory processes might have errors due to the reporting of data by third parties, primary sources etc. There is a chance of error in reporting data for various sectors. We do a thorough quality check to avoid errors. The quality and accuracy of emission inventory and associated errors are always reported by uncertainty, which is performed in the present study too. We have calculated the uncertainty based on used activity data and EFs used and reported in section 3.4.
It is unclear how the emission factors and GIS allocation are used. This refers to sections 2.2 and 2.3. The authors wrote that for the emission factors, “The appropriate scientific justification and judgement are discussed and elaborated”, however, I don’t find anywhere with more details. Also, the authors simply referred the method for emission estimate to another publication (Sahu et al., 2023a) without further introduction on the details. For section 2.3, half of the content is about the geography of India (721 districts, 5.89 million km road network, etc), but why they are introduced here? Why are they important to the method (or how relevant to the method)? I don’t find any information on how the authors created 0.1*0.1 degree emission map (is it a modeling approach or a statistical approach?).
Response: Thank you for your comment. Sections 2.2 and 2.3 state the emission factors and methodology that have been used to develop the national emission inventory. IPCC tier-2 approach is widely being used for reliable emission estimation where the emission is calculated for each source location based on fuel activity data and corresponding country-specific technological EFs. This bottom-up approach uses sector-specific technological emission factors and has historically been widely used across the globe to improve emission accuracy and uncertainty. We have reported this approach in detail in a previous couple of works of ours; hence, to avoid repetition we have provided recent references, where the same country-specific technological emission factors are being adopted to develop emission inventories for other Indian megacities. If the referee wishes then we can submit the EFs database as supplementary material.
Sahu et al, 2023 in ESSD, our recent paper has adopted similar EFs and methodology for megacity Delhi. To not repeat it, we have cited the paper for detailed EFs and methodology. If the reviewer wishes to add then we will add it again. Since the estimation of gridded emission includes such detailed proxy information we describe it in section 2.3. Now we have removed this information to the activity section under proxy data used.
The gridded (i.e. 0.10*0.10 degree) emission map is created based on a statistical approach where the source-specific (say transport, industry, residential cooking, MSW, Power etc.) emission layers are prepared based on spatial information of fuel activities and corresponding calculated emission. To prepare such a layer of information from various emission sources, the geospatial data includes information about road networks, geographic areas, population, and much more play a vital role. The generated, all of which is correlated with precise geographical places on the Earth's surface. These data serve as the foundation for allocating emissions spatially. Section 3.1. GIS-based spatial allocation of emission: contains every detail of how the authors created a 0.1×0.1 degree emission map.
The choice of the year 2020. The authors claimed 2020 as the base year for Indian PM emission. I’m not convinced about that as 2020 was the first year of COVID and the 2020 emissions are way lower than normal. I recommend the authors extend their work to longer time periods (normally more than 5 years) to meet the interest of the ESSD readers.
Response: To be clear, our data is generated for the period of 'April 2019 to March 2020' and considered as the base year 2020 which is purely without the COVID-19 pandemic and scenarios of a state-wide lockdown. Given that the lockdown in India started around the end of March 2020 and that emissions are not expected to change significantly. We are hopeful that the most recent estimates can accurately reflect post-pandemic possibilities. For this reason, we have set 2020 as the base year for current emissions, as stated in the manuscript's introduction. Although we welcome the recommendations to extend the period, it should be noted that developing emission inventories even for a year is a very complex process that takes a significant amount of time and effort. Definitely time series could have been intriguing to investigate the trend of emissions in countries like India, is not our focus due to the limitation of resources.
It is unclear about the uncertainty analysis. The authors should clarify in great detail the assessment of uncertainty, as it is important for emission inventory. Currently, the authors only mentioned in section 3.4 that they use Monte Carlo and error propagation.
Response: Thank you for a genuine comment; we have followed both the linear error propagation method and the Monte Carlo simulation methodology for the uncertainty estimation as recommended by IPCC.
In the linear error propagation method, individual source-specific percentage uncertainty of activity data (UAD) and the emission factor (UEF) are used. The Source-specific combined uncertainties (UC) are calculated using the formula –
The overall uncertainty (UI) of the inventory is calculated using the source-specific Emission (E) and the combined uncertainties (Uc).
In the Monte Carlo Simulation method, the source-specific activity data and the emission factors data are plotted and fitted to the five probability distribution functions viz. Normal distribution, Log-Normal Distribution, Student’s t-distribution, Triangular distribution and Uniform distribution. The output of the sector-specific uncertainties is calculated using the known function of each distribution. Every sectoral uncertainty output is iterated 100000 times and finally the mean, Standard deviation and 95% confidence interval is calculated. All the necessary statistical calculations are done in the IBM SPSS 24.0 (Paliwal et al., 2016).
Other major comments on the results and conclusion:
Section 3.3 compares this new inventory with other existing inventories. Large discrepancies are shown in many sectors. The authors should explain potential reasons for the difference, is it because of the methodology or model settings or something else? This section deserves more discussion.
Response: This is an expected comment and I must thank the reviewer. After a complete and thorough review of all past research works, it is observed that the large discrepancies are due to varying sectors included in different studies, fuel activity used, methodology used, adopted country-specific technological emission factors and varying base year. The activity data used for a couple of sectors has varying resolutions. Since the reported earlier studies have limited information on activity data, it is very difficult to compare directly. That is the reason the quality and accuracy of reported emission inventories are judged based on uncertainty, which is typically driven by emission factors and activity data used. We have addressed the issue in Section 3.3. In our comparison studies, we have highlighted the limitations and causes of discrepancy.
In the conclusion, the authors listed some recommendations for air quality mitigation. Those need to be more specific, such as what the outcomes or benefits would be if that recommendation is achieved (or to some extent). Without such information, I recommend removing the recommendations as they don’t seem to match the other parts of the manuscript.
Response: I agree with the reviewer, keeping the ongoing air quality issues in India, we believe the sectors responsible for such high particulate emission load are being quantified in the present study, which could be a very essential piece of information/data to policymakers. Indian cities are experiencing high particulate matter load and are among the list of most polluting cities across the world. Bringing focus to potential mitigating initiatives that could enhance the impact of developed data use for policymakers in India as well as for scientific communities across the globe. We've emphasized the source-specific mitigation strategy that can be implemented if steps are taken. As per suggestion, we will add more specific vehicle types and age-wise categories responsible for high emissions. Similarly, we will include more precise information for other sectors like Industry and residue.
Additionally, I recommend the authors improve the clarity of the presentation, I have difficulty understanding some of the sentences, and proper references are missing in many statements, below are some examples but not exhaustive:
Line 35: missing reference for ‘~4.2 million people die every year’
– WHO, 2022. We shall add the reference in manuscript too.
Line 60: confusing sentence, what does ‘die a premature death’ mean?
- The phrase “die a premature death” means someone dies at a younger age than expected or in an untimely manner effectively here causes of premature death include disease e.g. cancer, heart disease due to air pollution. We have changed it to “die prematurely” for more clarity. We recheck the manuscript for any such kind of mistake.
Lines 147-156: need to include version, year and reference for each source, and when cited in the later sections, include the references of these sources.
– As each source is discussed separately, the sectorial reference details are given. We have only stated the names in full in the aforementioned Line no. s in order to use them subsequently in an abridged form.
More examples come in the next section.
Specific comments:
Title: I suggest removing ‘comprehensive’
-As per suggestion we will remove “Comprehensive” from the title.
Line 38: ‘remains as’
-As per remark, It is replaced with “is”
Line 48: ‘air pollutant’ should be plural, same for later when it appears.
-We have corrected and replaced in all places in the manuscript.
Line 51: it should be consistent when using abbreviations, the authors should decide to use either the abbreviation “PM” or just “particulate matter” throughout the entire manuscript, but not mix them.
-As per suggestion, we have checked and corrected.
Line 58: remove ‘your’
-It is removed.
Line 62: why and how ‘chronologically’?
-It is removed from the sentence as just two studies are cited.
Line 81: ‘because of’ to ‘from’
-Replaced in the sentence.
Line 81: ‘it has’ to ‘they have’
-Replaced in the sentence.
Line 94: for ‘Sahu et al., 2021’, there are actually two Sahu et al., 2021 in the reference list, which one?
-This is supposed to be Sahu et al, 2021b and corrected in manuscript.
Lines 158-162: need some references
-Added to manuscript now
Line 167: classified by what feature?
-It is classified based on size, usage type, and technology. Now the sentence is being rewritten.
Line 171: ‘plying’?
“Plying” is run on road but for clarity, we have replaced with “running”
Figure 1: (1) for (b), I suggest plotting it by the order of contribution, i.e. Two wheeler (what is the percentage?), then next to 11.05%, then 4.51%, etc. (2) add (a)(b)(c) in the caption
-As per suggestion and remark, we have rectified in the manuscript.
Line 545: references for ‘widely used in emission inventories’
-Couple of references are added now
As per reviewer’s valuable suggestions, we have tried our best to incorporate in the manuscript.
Citation: https://doi.org/10.5194/essd-2023-310-AC1
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RC2: 'Comment on essd-2023-310', Anonymous Referee #2, 08 Nov 2023
Review for manuscript: ‘Development and comprehensive analysis of spatially resolved technological high resolution (0.1°×0.1°) Emission Inventory of Particulate Matter for India: A step Towards Air Quality Mitigation’
This publication deals with the development of a high resolution emission inventory for particulate matter (PM10 and PM2.5) over India for the year 2020. The work is very interesting since it deals with a highly polluted area in the world, thus having significant health related implications.
However, the manuscript requires major revisions in terms of clarity, methodological details and results presentation.
General comments:
-the methodology for emission estimation and the data sources used for each sector should be clarified. A Table could help in summarizing all the data sources.
-the methodology for producing spatially distributed emissions is not completely clear: do the authors apply a downscaling procedure of national emissions over the global gridmap at 10x10 km resolution? A Table could help to summarise approaches and data used for producing emission gridmaps.
-The authors should clarify the sector definition linking each sector to the corresponding IPCC categories (it can be done also in a supplementary table).
-the authors should explain why this work should be considered as a reference emission inventory for India or why it represents an improvement compared to existing inventories for India (e.g. REAS and others). Statistics used for estimating the emissions may not be complete and uncertain also in this work. Comments from the authors may provide indications on the limitations of this work.
-Why the authors provide estimates only on PM10 and PM2.5 and not on BC and OC?
-In order to run air quality models, all air pollutant emissions are needed over the domain of interest. How can regional air quality modellers use this work when only PM emissions are provided? Recommendations on which dataset could complement the current work for all other pollutants should be provided.
-the manuscript contains several English mistakes, typos, spaces missing between words etc. Accurate revision of the text is required before publication.
Detailed comments
-line 21: IPCC does not provide a methodology for estimating air pollutant (PM) emissions. Please clarify what is exactly meant here.
-the structure and story flow of the introduction is confusing and should be revised. It starts with a global picture, then it moves to India, then to South Asia…then it addresses again the health effects of PM and finally again back to India (although not explicitly mentioned).
-line 85: what is meant with ultra-precision?
-line 111: countries (e.g. in the world) or country (i.e. India)?
-lines 115-116: what type of sources are different from urban and rural sites? Is it mostly the relative share of the emissions being different between the 2 sites or are the sources different?
-line 125: remove major/minor.
-line 127 and 136 (and everywhere in the text when it appears): remove ‘based’ before EF
-line 136: what are secondary activity data? What is meant with secondary emissions?
-line 173: MT should be Mt and similarly everywhere in the text.
-line 220: replace decides with determines.
-line 303: do waste emissions belong to household or waste sector? Why are they included in households? Again linking to IPCC sectors would be useful.
-line 318: street vendor represents a specific activity for India and certain countries. However, it could be possibly related with emissions from combustion in commercial activities and services from sector 1A4 of IPCC. Is it correct?
-lines 342-344: not clear what was done to deal with missing/uncertain information.
-line 413 Kg should be kg. This correction should be applied everywhere in the text.
-line 428: it should read ‘and therefore waste is burned..’
-line 447: this paragraph discusses construction activities. What is the data source of the statistics used in the emission computation?
-Figure 2 presents several features of the Indian territory with maps. I think not all of them are relevant or discussed in the current work. I suggest including in the main text only those maps relevant for the emission work (e.g. removing water bodies, since no shipping emissions are discussed, etc.). The authors could split the figure to have first the characterization of the Indian territory, then the degree of urbanization and hen a focus on point sources, linear sources etc.
-line 537: actually no methodology and scientific justifications are presented. The authors should add a section on how emissions where calculated, data sources used, EFs values, and clarify any assumption made for each sector.
-section 2.3: it is not clear how spatially distributed emissions were calculated. Are they the result of the downscaling of a national total through the use of spatial proxies?
-section 3.3: in order to facilitate the comparison among the different inventories, I suggest including a Table summarizing for each sector the emission values provided by each inventory. REAS is used in the HTAP_v3 emission mosaic (https://essd.copernicus.org/articles/15/2667/2023/) as reference inventory for India. Why your study differs so much from REAS? A more consistent discussion should be provided in this section, using IPCC sectors to aggregate the emissions from different inventories. What is the reference year for your comparison? 2020 for all inventories? Figure 6 is not always clear, for example in the top panel power plant emissions for the current work are not visible. I suggest improving the visualization of these results.
-section 3.4 line 689: it is not clear from where uncertainty values for AD and EF are taken. References for uncertainty estimates should be provided.
-line 721: what is the contribution of super-emitting vehicles to PM emissions in India? I think adding a section in the transport sector description regarding super-emitters would be valuable.
-the conclusion section lists several mitigation measures. What is the feasibility of each of them? For example, how fuel adulteration could be monitored (lines 738-739)? Point b) is not clear. I suggest expanding this section including more details for each mitigation option.
-line 732: What is the usage of improvised public transport system? Do you mean ‘improved’?
-line 823 Crippa et al. 2019 refers to a paper on temporal profiles which are not discussed in the current work. If the authors are searching for a reference for the EDGAR inventory, they should mention the version used and include the corresponding citation (e.g. doi:10.1016/j.enpol.2022.113021, doi:10.5194/essd-10-1987-2018, https://edgar.jrc.ec.europa.eu/dataset_ap61).
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AC2: 'Reply on RC2', Saroj Kumar Sahu, 18 Nov 2023
- Thank you for the suggestion. The sources of the activity data have been listed next to each sector; however, in accordance with the reviewer's recommendation, we will compile the sources into a table and offer them as supplemental material to prevent repetition in the text. Since the methodology is an IPCC-defined bottom-up based standard method which is being traditionally used in developing emissions with minimum uncertainty, so we have elaborated further.
- Thank you for your remark. In order to improve the clarity, we have included more descriptions in explaining the proxy and digital data used for each sector like road network as proxy in transport sectors, forest cover, agriculture land, village level population and its political boundary. We haven't downscaled our national emissions from global gridmaps because doing so entails uncertainty in regional database emissions. In order to maintain the originality of the Indian emissions we have tried to develop the gridded (i.e. 0.1×0.1 degree) emission map using a GIS-based statistical approach in which source-specific emission layers (for example, transportation, industrial, residential cooking, MSW, Power, and so on) are prepared where the spatial information of each activity and related computed emissions is organized as a thematic layer. Geospatial data, which contains information about Indian road networks, geographic areas, population, and much more, is critical in preparing such a layer of information from diverse emission sources. The generated data with exact spatial location is key and foundation for spatially assigning emissions for each sector.
- As per the suggestion, we have provided the link to sectoral data used in the present calculation. The tabulated information is also attached as supplementary data (Table-1S).
Sl. No.
IPCC 2006 code
Sector
Source
1
1.A.3
Transport
Ministry of Road Transport & Highway
https://morth.nic.in/
Ministry of Statistics and Programme Implementation
https://www.mospi.gov.in/
2
EPA AP-42*
Wind-blown Road Dust
Ministry of Road Transport & Highway
https://morth.nic.in/
Indian Metrological Department’s (IMD)
https://mausam.imd.gov.in/
3
1.A.2
Industry
Ministry of Petroleum and Natural Gas
https://petroleum.nic.in/
Ministry of Micro, Small & Medium Enterprises
https://msme.gov.in/
Ministry of Statistics and Programme Implementation
https://www.mospi.gov.in/
4
1.A.1
Thermal Power Plant
Ministry of Power
https://powermin.gov.in/
Central Electricity Authority
https://cea.nic.in/?lang=en
5
1.A.4.b
Residential and Slum
Census of India
https://censusindia.gov.in/census.website/
Ministry of Housing and Urban Affairs
https://mohua.gov.in/
UN World Urbanization Prospects
https://population.un.org/wup/
7
1.A.4.a
Street Vendor
India’s Street Vending (Protection of Livelihoodand Regulation of Street Vending) Act https://mohua.gov.in/upload/uploadfiles/files/StreetVendorAct2014
8
3.C.1.b
Crop Residue Burning
Ministry of Agriculture & Farmers' Welfare
https://agricoop.gov.in/
Ministry of Statistics and Programme Implementation
https://www.mospi.gov.in/
9
1.B.1
Crematorium
Ministry of Home Affairs
https://www.mha.gov.in/en
SAFAR- Delhi (2018), Pune (2020)
10
Diesel Generator
International Energy Agency
https://www.iea.org/
Department of Telecommunication
https://dot.gov.in/
11
4.C.2
Municipal Solid Waste
Central Pollution Control Board
https://cpcb.nic.in/
12
4.C.1
Municipal Solid Waste Incineration Plant
Central Pollution Control Board
https://cpcb.nic.in/
13
1.B.1
Brick Kiln
Central Pollution Control Board
https://cpcb.nic.in/
Seay et al., (2021)
https://doi.org/10.1088/2515-7620/ac0a66
Rajarathnam et al., (2014)
https://doi.org/10.1016/j.atmosenv.2014.08.075
14
Cow Dung (Biofuel)
SAFAR- Delhi (2018), Pune (2020)
16
2.B.9.b
Incense stick/ Mosquito coil/ Cigarette
Cohen et al., (2013)
https://doi.org/10.1016/j.scitotenv.2013.03.101
Kumar et al., (2014)
http://dx.doi.org/10.4103/0972-6691.140770
17
1.A.2.k
Construction Activity
Central Pollution Control Board
https://cpcb.nic.in/
*US EPA AP-42 Section 13.2
- This is an expected comment and I must thank the reviewer for giving us a chance to explain. In the present inventory, we have considered spatial information of all activities under various sectors responsible for emission of particulate matter using an IPCC tier-2/3 based bottom-up technique, where country specific technological emission factors and resolution of the activity data is vital. The present approach not only improves the spatial information of emission hotspots due to inclusion of high-resolution proxy data and activity data but also reduces the uncertainty. Bottom-up emission inventories method entails gathering comprehensive information about sources and often rely on data at the source level, such as fuel usage, manufacturing processes, and activity levels like industrial type, capacity, production, fuel quantity used, vehicular type, technology used, age of vehicles, road condition (paved or unpaved), silt load in road, moisture pattern, cooking fuel types, aviation fuel, residue generated and burning pattern across the country, thermal power plants, waste burning and waste to energy generation etc., to calculate emission at source/grid level (i.e. bottom-up). This extensive information enables a thorough and sector-specific examination of emissions. It can, however, be resource-intensive and may necessitate significant data collection activities. Whereas in top-down approach estimation of emissions for a country is done at coarse level and is distributed based on few major proxies like population, urbanization and mostly land use pattern. This approach is adopted where the access to find resolution activity data is limited.
Unlike present emission inventory, REAS Emission Inventory is based on the IPCC-defined methodology where many sectoral emissions are calculated using top-down approach due to limitation of data. Moreover, many emission factors adopted are not country specific due to poor understanding of regional sources. Furthermore, the activity data for various sectors adopted from the international database rather than India-specific data, resulting in increased uncertainty. Since uncertainty is common in any inventory development due to inclusion of secondary sources, based activity data and emission factors adopted. Based on the best judgement and comparison, present inventory has less uncertainty. However, bottom-up emission inventories are widely being used by researchers, policymakers, and environmental agencies to examine the sources of emissions, identify areas for improvement, and devise strategies for minimizing environmental impacts. These inventories are critical in the development and assessment of policies aimed at controlling emissions and improving air quality.
- In India, PM is considered as a major pollutant responsible for changing air quality in many cities/megacities and modulating parameters in defining the Air Quality Index (AQI). In terms of health impact, both pollutants have significant impact and their sources are linked to similar kinds of sources in India. Development of emission inventory requires pollutant-wise country/sectoral specific technological EFs. In the case of PM10 and PM5, we have prepared a comprehensive database of all EFs through our scientific understanding and available data. Keeping the limitation of BC and OC in few major sectors (say limitation of OC EFs for transport sectors and diesel use in various sectors), we have not considered BC/OC along with gaseous pollutants in the present study but we are in process of reporting all other pollutants in our next manuscript. As a result, PM10 and PM2.5 remained our primary pollutant of interest to address the air quality issues in the country.
- I agree with the reviewer’s remark. Particulate matter (PM10, PM5) is the major pollutant in the Indian context. The developed high-resolution gridded dataset can be used along with other available global inventories like EDGAR/REAS etc. over India. Moreover, we are working diligently to develop the emission database for all other pollutants and will communicate very soon.
- We are sorry for the errors. We shall thoroughly look into these and make all corrections.
- I agree with the reviewer, the IPCC do not provide any specific methodology for pollutant specific estimation but approach to adopting a top-down or bottom-up approach for the development of the Emission Inventory is standard. Depending on the availability of detailed sector specific activity data (fine & coarse resolution) for a particular country, the approach is adopted to estimate emission. The IPCC report shows how to carry out the estimation process as per the proxy data availability, by differentiating it into Tier I, II, and III. We have adopted the IPCC-defined bottom-up approach for emission estimation based on Tier II and III data/EFs.
- We take the reviewer's suggestions into account. We will make necessary changes to the introduction section while preserving its proper flow and avoiding any detriment to the intended message.
- By ‘ultra-precision’ we tried to mean the fine resolution (0.1×0.1 degree) and spatially resolved emission hotspots. However, for clarity, we have replaced it with “spatially resolved” and rewrote the sentence.
- considering the remark, we will replace it to the country (India)
- India, as a developing country, has a both rural and urban population in its territory, with rural covering the larger proportion. When it comes to rural and urban areas, the types of sources do differ. The sources like transportation, industrial activity, and street vendors are more prominent in the urban area, whereas solid and biofuel usage in residential cooking activity, brick kilns, crop residue burning, mosquito coil burning etc. are the predominant sources of emission in the rural areas. So the share of emissions changes.
- Thank you for the remark. We shall consider the change.
- Thank you for the comment, we will remove the same throughout the manuscript.
- Secondary activity data is simply information obtained from a government sources and paid statistic data sites like indiastat etc. It has absolutely nothing to do with secondary emissions. We apologize for using a potentially confusing term. We shall modify the sentence in order to reflect its exact meaning.
- Thank you for the remark. We will make the corrections everywhere in the manuscript.
- Thank you for the remark. We shall consider the change.
- In response to the comment, we'd like to highlight a few key points. The waste sector of India deals with the waste generated especially from households as a major part as the population exceeds ~1.3 billion and is dumped at landfill sites. Since the major contributor of waste is household based activities and estimated accordingly. The gross solid waste generated from each household and applied to the waste sector as a whole. We have assigned the IPCC defined sector code 4.C.1 & 4.C.2 to this sector in a separate table.
- I agree with the reviewer’s remark. The Street vendor is related to the emission from commercial combustion activity similar to the IPCC 2006 code 1.A.4.a.
- In order to improve the missing/uncertainty information, data generated through our previous emission inventory campaigns were used to fill the gap and to improve the understanding about sources like data regarding the type of fuel used for commercial cooking and its consumption pattern by street vendors.
- Thank you for the remark. We will make the corrections everywhere in the manuscript.
- Thank you for the remark. We made the changes in manuscript.
- The report of Central Pollution Control Board - Guidelines on Environmental Management of Construction & Demolition (C & D) Wastes 2019-20 is the source. We shall add in the manuscript as well.
- Water bodies were used mask out the areas where the emission are not likely to happen. This one of the important component of land Use land cover pattern and are used indirectly improve the spatial emission. If reviewer wish to remove it then we will remove it.
- Our recent manuscript i.e. Sahu et al, 2023 in ESSD, has adopted similar EFs and methodology where the emissions were estimated at 400mt resolution for National capital megacity “Delhi”. To not repeat it, we have cited the paper for detailed EFs and methodology. If the reviewer wishes to add then we will add it again.
- Thank you for your concern regarding Section 2.3, which states about the emission factors and methodology that has been used to develop the national emission inventory. IPCC tier-2/3 approach is widely being used for reliable emission estimation where the emission is calculated for each source location based on fuel activity data and corresponding country specific technological EFs. This bottom-up approach uses sector specific technological emission factors, and has historically been widely being used across the globe to improve the emission accuracy and uncertainty. We have reported this approach in detail in the previous couple of works of ours; hence, to avoid repetition we have provided recent references, where the same country specific technological emission factors are being adopted to develop city emission for other Indian megacities. If the referee wishes then we can submit the EFs database as supplementary material.
As mentioned earlier, Sahu et al, 2023 in ESSD, our recent paper has adopted similar EFs and methodology for megacity Delhi. In order to not repeat it again, we have cited the paper for detailed EFs and methodology. If the reviewer wishes to add then we will add it again. Since the estimation of gridded emission includes such detailed proxy information so we describe it in section 2.3. Now we have removed this information to the activity section under proxy data used.
The gridded (i.e. 0.1*0.1 degree) emission map is created based on a statistical approach where the source specific (say transport, industry, residential cooking, MSW, Power etc.) emission layers are prepared based on spatial information of fuel activities and corresponding calculated emission. To prepare such a layer of information from various emission sources, the geospatial proxy data includes information about road networks, location of industries, forest/agriculture land cover, geographic areas, population, and much more play a vital role. The generated dataset is correlated with precise geographical places on the Earth's surface. These data serve as the foundation for allocating emissions spatially. Section 3.1. GIS based spatial allocation of emission: contains every detail of how the authors created 0.1*0.1 degree emission map.
- We thank the referee for suggestions. We will put the values in a table and also provide revised plot (Figure 6). Moreover, major sectors in REAS inventory are based on the IPCC-defined Top-down approach for emission estimation. Moreover, the activity data for various sectors adopted from the international database rather than India-specific data, resulting in increased uncertainty. Since each inventory is developed for a different base year with different sets of activity data and emission factors, there is a large disparity in reported emission by various research communities. Present inventory is estimated for 2020, which is the most recent year where country specific technological EFs are adopted for 17 sectors which is first of its kind emission dataset reported for India. In our case, base year 2020 includes data from (April 2019 to March 2020) and as there is no significant shift in emission this will accurately reflect post-pandemic emission scenarios, as stated in the manuscript's introduction section.
- As per suggestion it is provided in the manuscript. We used both the linear error propagation approach and the Monte Carlo simulation methodology for estimating uncertainty, as recommended by the IPCC and widely used by emission inventory research communities. Both Individual source-specific percentage uncertainty of activity data (UAD) and the emission factor (UEF) is adopted. The Source-specific combined uncertainties (UC) are calculated using the formula -
The overall uncertainty (UI) of the inventory is calculated using the source-specific Emission (E) and the combined uncertainties (Uc).
In the Monte Carlo Simulation method, the source-specific activity data and the emission factors data are plotted and fitted to the five probability distribution functions viz. Normal distribution, Log-Normal Distribution, Student’s t-distribution, Triangular distribution and Uniform distribution. The output of the sector-specific uncertainties is calculated using the known function of each distribution. Every sectoral uncertainty output is iterated 100000 times and finally the mean, Standard deviation and 95% confidence interval is calculated. All the necessary statistical calculations are done in the IBM SPSS 24.0 (Paliwal et al., 2016).
- Vehicles older than 15 years old (super-emitters) account for about 42% of total vehicular PM10 emissions. We will include a paragraph in this context in the document, as suggested by the reviewer.
- This is a very good suggestion. We appreciate the reviewer’s remark. However, the editor of ESSD suggests that this is a dataset paper and the mitigation measures do not comply with the theme of the manuscript. They want it to be removed. So, we are removing the whole mitigation section from the text.
- Yes, a seamless and upgraded (improved) public transportation system is critical in India in order to limit the use of private automobiles and encourage the people to drive using public transportation. Here improved means of public transport with new fleets with cleaner fuel and better emission norms, which will lower vehicular emissions to some extent. We shall replace the term ‘improvised’ to ‘improved’.
- Thank you very much for your genuine comment. We will replace the reference with the suggested one and use the version now.
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AC2: 'Reply on RC2', Saroj Kumar Sahu, 18 Nov 2023
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EC1: 'Comment on essd-2023-310', Tobias Gerken, 12 Nov 2023
We have now received two referee reports which are generally supportive of the idea and the work conducted. I would like to thank the reviewers for their careful consideration of this manuscript.
Both referee reports highlight the need to better communicate the underlying methodology for calculating the emission dataset and to improve the clarity of the manuscript. I recommend that the authors carefully consider the referee comments when preparing their response and revised submission. In line with the authors' desire to provide a basis for mitigating air quality issues, it is key that potential users and the scientific community at large have a complete understanding on the creation of this dataset including data sources and their processing. Ideally this would include the software/ code that is used to generate the dataset.
Additionally, because this is manuscript submitted to a data journal introducing a new data set, I would strongly suggest removing recommendations to improve air quality, which seem out of place in this context and should be more carefully backed up by scientific results outside of a data paper.
Citation: https://doi.org/10.5194/essd-2023-310-EC1 -
AC3: 'Reply on EC1', Saroj Kumar Sahu, 18 Nov 2023
We are thankful that both referees are supportive of our work. As per suggestion, we have tried our level best to make necessary changes in the manuscript so that methodology and data used should be more transparent. I welcome the Editor’s suggestion to remove the mitigation section and we have removed it from the manuscript. The whole emission inventory process does not follow any software code but it is a statistical approach to process the sectoral emission estimation in the GIS environment and converting into desired format as per need. Here we have prepared gridded emissions for further use in any kind of model. The line numbers (735-767) is removed from the manuscript.
Citation: https://doi.org/10.5194/essd-2023-310-AC3
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AC3: 'Reply on EC1', Saroj Kumar Sahu, 18 Nov 2023
Data sets
High-resolution Gridded Emission Database for India Saroj Kumar Sahu https://doi.org/10.5281/zenodo.7885103
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