The NOx point source catalog is based on NO2 TVCDs from TROPOMI for the years 2018–2019, using the offline product (with successively increasing algorithm version from 0.11.0 on 1 January 2018 to 1.3.0 on 31 December 2019), as provided by KNMI/ESA via http://copernicus.eu (last access: 21 June 2021). Details of the TROPOMI tropospheric NO2 product are given in and .

TROPOMI is flying on a sun-synchronous orbit with a local overpass time of about 13:30. The pixel size at nadir was 7.2 km × 3.6 km initially and even improved to 5.6 km × 3.6 km from 6 August 2019 on . TROPOMI provides daily global coverage, resulting in quite good statistics already for annual means.

Horizontal wind fields u and v as well as air pressure p and air temperature T are taken from reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Wind fields are required for the calculation of fluxes. Pressure and temperature are needed for scaling NO2 up to NOx, which involves (a) the reaction rate constant of NO+O3 as function of T and (b) the conversion of climatological O3 mixing ratios to concentrations depending on T and p (see Sect ).

Until August 2019, ERA-Interim data are used with a truncation at T255, corresponding to ≈0.7∘ resolution. Since September 2019, ERA-5 data are used with a truncation at T639, corresponding to ≈0.3∘ resolution . For both datasets, a preprocessed dataset was created where the 6-hourly model output (00:00, 06:00, 12:00, and 18:00 UTC) was interpolated to a regular horizontal grid with a resolution of 1∘. For future processing, sampling will be based on finer temporal and spatial grids.

Ozone mixing ratios, used for the scaling of NO2 to NOx, were taken from the Earth System Chemistry integrated Modelling (ESCiMo) project , using the RC1SD-base-10a simulation for the years 2000–2010. The monthly mean climatology was calculated from the model fields sampled online along the overpass time of OMI aboard Aura (which is close to the TROPOMI overpass time) using the MESSy SORBIT submodel . As the divergence is sensitive for the added NOx at the source, the relevant NOx/NO2 ratio is that close to ground. We thus took O3 concentrations from the lowest model layer.

The World Resources Institute provides an open-access Global Power Plant Database (GPPD) . We use this database (v1.2) in order to automatically identify NOx point sources corresponding to power plants.

The GPPD lists almost 30 000 power plants of all kinds, including solar, nuclear, and hydro power. For our purpose, we created a subset of those power plants using coal, gas, or oil as primary fuel. In addition, power plants with capacities below 100 MW are skipped. The resulting subset of GPPD comprises 4654 power plants of which 2013, 2265, and 376 use coal, gas, and oil as primary fuel, respectively.

For this study, we select TROPOMI tropospheric NO2 column densities VNO2 for the years 2018–2019 with values of the data quality indicator (“qa value”) above 0.75, as recommended in , and effective cloud fractions (CF) below 0.3. These selection criteria are the same as in .

In addition, we skip measurements with solar zenith angle (SZA) above 65∘ for the calculation of fluxes. This strict criterion removes observations for sun being low and implicitly results in a gradual removal of wintertime measurements for midlatitudes, while in , winter months have been skipped explicitly for Germany. Wintertime measurements are skipped in order to avoid unfavorable viewing conditions, snow-covered scenes, and stronger interference with aged plumes due to longer lifetimes. Moreover, the SZA restriction allows us to simply parameterize the NO2 photolysis as a function of the SZA (see Sect. ).

Since large parts of the globe are free from stationary NOx point sources, in particular oceans, deserts, and forests, the processing focuses on potentially stationary sources. For this purpose, a selection mask is defined (Fig. ), which is based on magnitude as well as the temporal variability of TROPOMI NO2 TVCDs. The construction of the selection mask is explained in detail in the Supplement.

In order to have maximum sensitivity for point sources, the TROPOMI observations have to be oversampled, requiring a fine grid resolution of less than 3 km. For each TROPOMI orbit, VNO2 is thus gridded to a regular longitude/latitude grid with 0.025∘ resolution for 61∘ S to 61∘ N. Note that there are a few small NOx sources north of 61∘, but due to the strict SZA threshold of 65∘, the flux statistics would be poor for higher latitudes.

Gridding is done per orbit based on linear 2-D interpolation of TROPOMI pixel centers using the griddata function from the Python module SciPy . This approach allows for fast gridding. In addition, there are no discontinuities at the TROPOMI pixel borders, which would lead to extremely high (positive and negative) values of the derivative.

All missing values (qa < 0.75) as well as the outermost pixels on each side of the TROPOMI swath (i.e., the pixels with the highest viewing zenith angles) are set to not a number (NaN). This is necessary in order to restrict the area of interpolated TVCDs to the actual area covered by measurements.

Hereafter we denote the longitude and latitude dimensions as x and y, respectively, in vector indices as well as in the text.

The meteorological datasets u, v, p, and T are extracted from ECMWF input data by linear interpolation in three steps:

In vertical dimension to an altitude of 300 m above ground for each ECMWF input dataset with 6-hourly resolution. As emissions from point sources are the focus of this study, we consider wind fields representative for the transport of freshly released NOx emissions from stacks. The choice of altitude of wind fields is further discussed in Sect. . Only pixels with the mask M ≥ 1 are extracted.

In this study we scale the measured NO2 TVCD to a NOx TVCD for each TROPOMI pixel. The conversion factor L is calculated according to the photostationary steady state: 2L:=[NOx][NO2]=1+[NO][NO2]=1+Jk[O3].

The impact of volatile organic compounds (VOCs) is neglected here as the focus is put on NOx point sources and thus generally high NOx concentrations.

The photolysis frequency of NO2 J is parameterized as a function of the SZA θ by 3J=0.0167exp⁡(-0.575/cos⁡θ)s-1 as proposed by . This parameterization is “accurate to about 10 % for mostly sunny conditions” for SZA < 65∘ .

The reaction rate constant k for the reaction of NO with O3 is parameterized as a function of temperature (in kelvin) by 4k=2.07×10-12×exp⁡(-1400/T), following the recommendations from and .

Near-surface ozone mixing ratios are taken from a climatology based on the ESCiMo model simulation (Sect. ) and converted into concentrations based on T and p from ECMWF.

The derived values for L represent conditions for near-surface pollution. For background NOx in the upper troposphere, the partitioning would be shifted towards NO. However, any additive background is automatically removed by the calculation of the divergence. Thus, the partitioning derived for near-surface concentrations is appropriate also for correcting the added column caused by a point source.

Figure  displays the ratio of temporal means of NOx and NO2 TVCDs. Global mean is 1.35 with a SD of 0.08. Values for Riyadh, South Africa, and Germany are 1.22, 1.36, and 1.41, respectively, in agreement with the value of 1.32 ± 0.26 applied in , which was based on the number given in for polluted conditions around noontime.

Note that the actual [NOx] / [NO2] ratio close to a point source might be different in the case of high NO concentrations causing O3 titration. In this case, however, the divergence method would detect the emitted NOx downwind from the source as soon as the NO is converted to NO2 after mixing with ambient air. This results in a spatial smearing of the peak in the divergence map, leading to broader peaks, but the same integral (and thus emissions) for the peak fitting algorithm (Sect. ). For the final budget of NOx emissions, which are determined from the integrated peaks, the final photostationary state is thus still adequate.

From gridded NOx columns and gridded wind fields, the gridded NOx flux in both the x and y direction is derived for each TROPOMI orbit. Mean fluxes are calculated for the period 2018–2019, where calm wind conditions (w<2 m s-1) are skipped. For grid pixels with less than 25 measurements, fluxes are set to missing values due to poor statistics. Note that we do not explicitly skip winter months for midlatitudes, as in for Germany, but they are removed implicitly by the strict SZA threshold of 65∘.

From the mean zonal and meridional flux maps, the divergence map D=∇⋅F is calculated, which is the basis for the identification and quantification of point sources below.

In , emission maps were derived by adding the sink term S=V/τ to the divergence map. For this, a constant lifetime of τ=4 h was applied, as derived from OMI data for Riyadh . In addition, V was corrected by subtracting the regional background, as the lifetime estimate was derived for freshly released, near-surface pollution, while upper-tropospheric background NO2 generally has a longer lifetime.

As discussed in , the inclusion of the sink term has significant impact on area sources; it contributes about 50 % of integrated emissions for the Riyadh urban area. For point sources, however, the emission signal is by far dominated by the divergence term, for instance accounting for 87 % of the emissions from power plant “PP9” in .

Within this study, we do not correct for the sink term S for the following reasons:

The NOx lifetime is expected to be different for the diverse conditions in the considered regions, covering a large variability of temperature, humidity, actinic flux, VOC levels, and NOx levels. Note that the expected strong dependency of mean lifetime on latitude is again suppressed here due to the selection of SZA < 65∘.

The resulting low bias of point source emissions caused by the missing lifetime correction is discussed in Sect. .

Since no lifetime correction with S=V/τ is performed, also the background correction for V, which was performed in in order to exclude upper-tropospheric NOx with longer lifetime and different L, is omitted in the current study. Note that the local background of V, as well as any potential offset due to, e.g., stratospheric correction, would affect the lifetime correction but have no impact on D, as any additive term is lost by the calculation of the derivative.

Tropospheric column densities of NO2 are derived from the total slant column by subtracting the stratospheric column and applying the so-called air mass factor (AMF). The AMF can be derived as the sum of height-dependent ”box AMFs”, representing the vertical measurement sensitivity, weighted by the relative profile . In the operational TROPOMI NO2 product, averaging kernels (AKs) are provided that are proportional to box AMFs and allow us to correct the AMF for a different vertical profile .

Validation studies report on a general low bias of NO2 TVCD from TROPOMI of a factor of 2 and more for polluted sites e.g., , caused by a high biased AMF. Part of this bias seems to be related to the a priori profiles which do not resolve the pollution profiles close to sources. In addition, there are indications that the cloud heights used for the TVCD retrieval are biased low and the albedo maps used are biased high , resulting in biased AKs.

In , an AMF correction was performed for South Africa and Germany by applying the provided AK to a near-surface profile. In this study, we do not apply such an AMF correction, as the effects of biased input albedo and cloud height cannot be corrected a posteriori based on the provided AKs but require a reprocessing of the TROPOMI NO2 product.

Consequently, the low bias of TROPOMI TVCDs is directly transferred into the NOx emissions listed in the catalog, as discussed in detail in Sect. . The low bias is expected to be improved with an updated NO2 product, which will then be used for deriving an updated version of the point source catalog.

We apply a fully automated iterative peak fitting algorithm in order to detect NOx point sources. The goal is to identify clear point source peaks in the divergence map, where a robust quantification of emissions is possible, while ambiguous cases are skipped. Thus, the resulting catalog of point sources is incomplete; a detailed discussion on various reasons for missing point sources is given in Sect. . But the remaining point sources listed in the catalog correspond to actual NOx sources with high confidence.

In each iteration, the following procedure is executed:

The grid pixel with highest value of the divergence is considered the point source candidate.

We perform an automated match of the point source catalog with the combustion power plants listed in GPPD. For each point source, we search for GPPD entries within 5 km radius. In the point source catalog, we add

the integrated capacity of all power plants within 5 km,

The iterative peak fitting algorithm yields 7250 candidates, of which 451 are classified as point sources. For 242 of these point sources, a match with GPPD power plants was found. Table  lists the classification statistics for the regions defined in Table .

Figure  displays regional maps of color-coded D for selected regions. The respective maps for all regions listed in table are provided in the Supplement in PDF format, allowing for loss-free zooming.

The candidate classifications are indicated by symbols, where point sources with/without a power plant match are displayed as triangles in magenta/dark grey, respectively. Non-point sources are shown in light grey. For sake of clarity, they are only displayed for high divergence values (Dmax>1µg m-2 s-1 or integrated D>0.1 kg s-1), as they would otherwise dominate the figure for some regions like Europe, where 499 candidates are classified as “gap” and 1013 as “negative” (Table ).

The map of the Middle East (a) contains most of the non-point-source examples shown in Fig. . Several more gaps occur all along the Persian Gulf coastline, and further negative candidates are found in mountains as well as along the coastlines.

Several large cities like (a) Cairo and Jeddah, (b) Paris and Madrid, (c) Delhi and Mumbai, or (d) Moscow and Saint Petersburg are categorized as area source. However, there are also some candidates categorized as area source which do not correspond to a megacity. In particular the candidate corresponding to the maximum divergence over India, which is caused by the coal-fired 5 GW Vindhyachal Super Thermal Power Station, was categorized as area source, as it interferes with the 4 GW Anpara power plant about 16 km northeast (Fig. d). Such sources could still be investigated in detail based on the divergence map. However, for interfering sources so close to each other, a quantification by a fully automated algorithm is challenging.

For Riyadh, the power plants PP9 and PP10 northeast and southeast of the city center are identified as point sources (compare ). In contrast to , PP8 west of Riyadh is not identified as a point source as it could not be separated from Riyadh city, as a consequence of the strict pre-selection of candidates and the slightly larger fit interval, which were necessary in order to run the algorithm fully automated globally.

Several point sources are detected in the Middle East. There is a remarkable cluster of several point sources detected south of Baghdad. Note that there was even a point source detected within the Persian Gulf, which corresponds to the Zakum offshore oilfield.

The Indian subcontinent reveals the highest number of point sources of the investigated regions, contributing one-fourth of the global number. This reflects the quickly growing industrial activities, while measures for emission reduction still need to catch up. In addition, the divergence method obviously works very well for India, as the noise in divergence is quite low. This might be related to the dry season providing very good observation conditions without gaps, thereby suppressing sampling effects (compare Sect. ).

In Europe, only very few point sources are detected, like the world's largest charcoal power plant Bełchatów in Poland; the German charcoal power plants Jänschwalde, Boxberg, and Neurath/Niederaußem compare; or Europe's largest steel plant in Taranto, southern Italy. Remarkably, almost no point sources are detected for England and the Benelux countries, where the mean TVCD has a local maximum (Fig. a). Instead, there are several candidates classified as “gaps” and “negative”, which is related to the high noise observed in the divergence map. A similar situation is found for China, where TVCD is highest globally (Fig. a), but noise in D is large (Fig. S11 in the Supplement) such that the number of negative candidates is high (1013), but only few (34) point sources could be clearly identified. In Ukraine and western Russia, where mean TVCD levels are moderate, several point sources could be clearly identified. These striking regional differences in the performance of the automated point source detection will be discussed in detail in Sect. .

We derive a global catalog of NOx emissions from the 451 peaks categorized as point sources by sorting them according to the fitted emissions. A complete list of all detected point sources, including latitude/longitude and the estimated emissions, is provided in CSV format at https://doi.org/10.26050/WDCC/Quant_NOx_TROPOMI and also added as a data file to the Supplement.

Table  lists a selection of the identified point sources with some additional information on the respective NOx source. It contains the top 10 emitters of the catalog. In addition, every 100th rank is included in order to illustrate conditions for lower divergence levels. Divergence maps for the same selection are shown in Fig. , where also external information on NOx sources have been added. In the Supplement, respective divergence maps and tables of regional top emitters are shown for all considered regions.

Most of the point sources listed in Table  can actually be associated with single or groups of power plants. Overall, a GPPD match was found for 242 point sources. The median distance between GPPD and point source locations was found to be 1.6 km, which is better than TROPOMI resolution. For the selection in Table , we did some additional inquiry and could identify the power plants Medupi (no. 3) and Presidente Vargas (no. 300), both missing in GPPD, as probable NOx sources.

Other point sources are the Secunda CTL coal liquifying facility (no. 2), steel work facilities (no. 6, no. 10), and a cement plant (no. 100). Point source no. 7 is located in Ulsan, an industrial hotspot in South Korea. Here, however, we could not identify a single dominating NOx point source. For the Hermosillo power plant, the peak fit is probably affected by Hermosillo city nearby (0.7 million inhabitants).

The four highest point source NOx emissions are all found for South African coal power plants, which have already been presented in . Note that the emissions in Table  are lower than those given in for various reasons, as discussed in detail in Sect. , mainly due to the missing AMF and lifetime corrections in the current study.

The thresholds for artifacts in divergence have been defined rather strictly. Consequently, the remaining locations listed in the catalog actually indicate stationary NOx emissions. In the spot tests investigated exemplarily, we found no indication for false signals in the catalog.

Figure a provides a scatter plot of power plant capacity and point source emissions. Respective regional figures for all considered regions are provided in Fig. S15 in the Supplement.

Note that a perfect correlation between power plant capacity and NOx emissions cannot be expected, as the emissions per capacity strongly depend on fuel type and technology and are particularly modified if emission control measures like selective catalytic reduction (SCR) are applied. High emissions for power plants with low capacity probably indicate other dominating NOx sources nearby, like for Baotou (no. 6), where a 0.2 GW power plant was matching the point source location, but emissions are probably mainly caused by the metal smelting facilities. Low emissions from high-capacity power plants probably indicate the installation of SCR or a recent reduction in capacity.

High correlations can be observed for some regions like South Africa and Australia. This is probably indicating that the GPPD entries are reliable for these regions, the level of power plant technology is regionally similar, and the divergence method works well here. Figure b displays the scatter plots for coal-fired power plants for Australia, Europe, and South Africa. The slopes indicate clear regional differences in the emissions-per-capacity ratio.

When interpreting the presented point source catalog, the following limitations have to be kept in mind.

The main goal of v1.0 of the point source catalog is the identification rather than the quantification of NOx point sources. Still we discuss and try to quantify the various sources for uncertainties of the derived point source emissions.

Though the catalog is incomplete and the derived emissions are biased low, it still has the potential to improve our knowledge on NOx emissions. Here we discuss the benefits of the divergence approach and the point source catalog and propose future applications.

This paper describes v1.0 of the NOx point source catalog. We plan to update the catalog as soon as a reprocessed TROPOMI NO2 product is available. We expect that some of the persistent gaps along coastlines, e.g., around Dubai, can be closed in the future, as soon as the cloud information is based on high-resolution maps of the surface albedo, and thus NO2 TVCD becomes available there. In addition, improved surface albedo, cloud height, and a priori profiles are expected to improve the TVCD and (at least partly) remove the low bias.

In addition, we plan to use meteorological data from ERA-5 on higher spatial and temporal resolution. Currently, 6-hourly model output of ECMWF meteorological data was interpolated to a regular horizontal grid with a resolution of 1∘. Case studies will be performed in order to find out which is the best compromise between spatiotemporal sampling and processing time.