High-resolution Emission Inventory Research Articles

Underestimation of Thermogenic Methane Emissions in New York City.

Recent studies have shown that methane emissions are underestimated by inventories in many US urban areas. This has important implications for climate change mitigation policy at the city, state, and national levels. Uncertainty in both the spatial distribution and sectoral allocation of urban emissions can limit the ability of policy makers to develop appropriately focused emission reduction strategies. Top-down emission estimates based on atmospheric greenhouse gas measurements can help to improve inventories and inform policy decisions. This study presents a new high-resolution (0.02 × 0.02°) methane emission inventory for New York City and its surrounding area, constructed using the latest activity data, emission factors, and spatial proxies. The new high-resolution inventory estimates of methane emissions for the New York-Newark urban area are 1.3 times larger than those for the gridded Environmental Protection Agency inventory. We used aircraft mole fraction measurements from nine research flights to optimize the high-resolution inventory emissions within a Bayesian inversion. These sectorally optimized emissions show that the high-resolution inventory still significantly underestimates methane emissions within the New York-Newark urban area, primarily because it underestimates emissions from thermogenic sources (by a factor of 2.3). This suggests that there remains a gap in our process-based understanding of urban methane emissions.

Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL

Abstract. To design a monitoring network for estimating CO2 fluxes in an urban area, a high-resolution observing system simulation experiment (OSSE) is performed using the transport model Graz Mesoscale Model (GRAMMv19.1) coupled to the Graz Lagrangian Model (GRALv19.1). First, a high-resolution anthropogenic emission inventory which is considered as the truth serves as input to the model to simulate CO2 concentration in the urban atmosphere on 10 m horizontal resolution in a 12.3 km × 12.3 km domain centred in Heidelberg, Germany. By sampling the CO2 concentration at selected stations and feeding the measurements into a Bayesian inverse framework, CO2 fluxes on a neighbourhood scale are estimated. Different configurations of possible measurement networks are tested to assess the precision of posterior CO2 fluxes. We determine the trade-off between the quality and quantity of sensors by comparing the information content for different set-ups. Decisions on investing in a larger number or in more precise sensors can be based on this result. We further analyse optimal sensor locations for flux estimation using a Monte Carlo approach. We examine the benefit of additionally measuring carbon monoxide (CO). We find that including CO as tracer in the inversion enables the disaggregation of different emission sectors. Finally, we quantify the benefit of introducing a temporal correlation into the prior emissions. The results of this study have implications for an optimal measurement network design for a city like Heidelberg. The study showcases the general usefulness of the inverse framework developed using GRAMM/GRAL for planning and evaluating measurement networks in an urban area.

Carbon and air pollutant emissions forecast of China's cement industry from 2021 to 2035

The cement industry is a major source of carbon dioxide and air pollutant emissions. This study developed a high-resolution emission inventory for China's cement industry and forecasted emissions from 2025 to 2035. The results showed that emissions in 2020 were 251, 709, 142, and 1.35 × 106 Gg for SO2, NOx, PM2.5, and CO2, respectively. The optimal model projections for 2035 showed that fuel and clinker substitutions had the best reduction effect on SO2 and CO2, with average reduction rates of –11.42 % and –7.37 %, respectively, compared to the 2035 frozen scenario. Pollutant control measures and clinker substitution had the best emission reduction effect on NOx and PM2.5, and the average reduction rates were –26.09 % and –4.23 %, respectively, compared to the 2035 frozen scenario. These results showed that the substitution of fuel and clinker has important co-benefits for air pollutants in the cement industry.

High Resolution Emission Inventory of Greenhouse Gas and Its Characteristics in Guangdong，China

Based on the typical city survey data and statistics of Guangdong Province， a 2018-based 3 km×3 km gridded greenhouse gas emissions inventory was developed for Guangdong Province using the combination of top-down and bottom-up emission factor methods. The inventory covered the CO2， CH4， and N2O emissions from energy， industrial processes， agriculture， land use change and forest， waste management， and indirect sources. The results showed that estimates for CO2， CH4， and N2O in Guangdong Province for the year 2018 were 8.5×108， 1.9×106， and 1.1×105 t， respectively， and 8.5×108， 4.0×107， and 3.4×107 t by equivalent carbon dioxide， totaling 9.2×108 t. CO2 was the main greenhouse gas in Guangdong Province， accounting for 92.0% of the total emissions. Energy and indirect sources were the main emission sources， accounting for 77.9% and 7.6%， respectively， totaling 85.5%. Spatial distributions illustrated that most grids were greenhouse gas emissions， whereas some others were greenhouse gas sinks； the greenhouse gas emissions were distributed mainly in the Pearl River Delta region and had certain characteristics of distribution along the road network and channels. The greenhouse gas grids of high emission were mainly the locations of high energy-consuming enterprises such as large power plants， steel mills， and cement plants.

Unveiling the Spatial-Temporal Characteristics and Driving Factors of Greenhouse Gases and Atmospheric Pollutants Emissions of Energy Consumption in Shandong Province, China

As the largest energy-consuming province in China, Shandong faces the dual task of greenhouse gas (GHG) reduction and atmospheric pollution control. Based on the latest activity data and updated emission factors, this study establishes a high-resolution emission inventory (5 km × 5 km) for GHGs and main atmospheric pollutants from the energy consumption sectors of Shandong Province from 2010 to 2021, quantifies the relationship between social economic factors and GHGs and atmospheric pollutants emissions using the expanded stochastic environmental impact assessment (STIRPAT) model, and forecasts the future emission trend with the help of the scenario analysis method. Results indicate that the electricity and transportation sectors are the main contributors to all pollutants. Spatially, the high value of pollutants is mainly concentrated in the urban agglomerations of central and eastern Shandong. Up to 72% of GHGs and 50% of air pollution emissions are attributed to the top 10% of emission grids. Emission peaks occur mainly in summer and winter due to straw burning, increased utilization of temperature-controlled facilities, and expansion of plant capacity. Population, energy consumption, the proportion of secondary industry, and energy consumption intensity are the most significant influencing factors for pollutant emissions. Scenario analysis results indicate Shandong province can reach its carbon peak in 2027 without sacrificing population growth or economic progress.

Estimation and Analysis of Air Pollutant Emissions from On-Road Vehicles in Changzhou, China

Vehicle emissions have become a significant contributor to urban air pollution. However, studies specific to city-level vehicle emission inventories are still scarce and tend to be outdated. This study introduces a methodology for developing high-resolution monthly vehicle emission inventories. We applied this methodology to Changzhou in 2022 to analyze emission characteristics and generate gridded emission data with a resolution of 0.01° × 0.01°. The results show that the total vehicle emissions of carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NOx), and fine particulate matters (PM2.5) in Changzhou are 39.69, 8.68, 18.6, and 0.56 Gg, respectively. Light-duty passenger vehicles are the main contributors to CO (74.3%) and VOCs (86.1%) emissions, while heavy-duty trucks play a significant role in NOx (50.7%) and PM2.5 (34.7%) emissions. Gasoline vehicles are mainly responsible for CO (78.6%) and VOCs (91.4%) emissions, while diesel vehicles are the primary source of NOx (81.1%) and PM2.5 (70.6%) emissions. Notably, China IV vehicles have the highest emission contribution rates (ranging from 32.5% to 44.9%). Seasonally, emissions peak in winter and are lowest in April. Spatially, emission intensity is higher in the northeast of Changzhou compared to the west and south. The methodology presented in this study offers a valuable tool for developing comprehensive city-level emission inventories, and the results provide critical insights that can inform the formulation of effective environmental policies.

Establish of air pollutants and greenhouse gases emission inventory and co-benefits of their reduction of transportation sector in Central China

Recently, the transportation sector in China has gradually become the main source of urban air pollution and primary driver of carbon emissions growth. Considering air pollutants and greenhouse gases come from the same emission sources, it is necessary to establish an updated high-resolution emission inventory for the transportation sector in Central China, the most polluted region in China. The inventory includes on-road mobile, non-road mobile, oil storage and transportation, and covers 9 types of air pollutants and 3 types of greenhouse gases. Based on the Long-range Energy Alternatives Planning System (LEAP) model, the emissions of pollutants were predicted for the period from 2020 to 2035 in different scenarios. Results showed that in 2020, emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, NH3, BC, OC, CO2, CH4, and N2O in Henan Province were 27.5, 503.2, 878.6, 20.1, 17.4, 222.1, 21.5, 9.4, 2.9, 92,077.9, 6.0, and 10.4 kilotons, respectively. Energy demand and pollutant emissions in Henan Province are simulated under four scenarios (Baseline Scenario (BS), Pollution Abatement Scenario (PA), Green Transportation Scenario (GT), and Reinforcing Low Carbon Scenario (RLC)). The collaborative emission reduction effect is most significant in the RLC scenario, followed by the GT scenario. By 2035, under the RLC scenario, energy consumption and emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, NH3, CO2, CH4, and N2O are projected to decrease by 72.0%, 30.0%, 55.6%, 56.0%, 38.6%, 39.7%, 51.5%, 66.1%, 65.5%, 55.4%, and 52.8%, respectively. This study provides fundamental data support for subsequent numerical simulations.

A high-resolution (0.1° × 0.1°) emission inventory for the catering industry based on VOCs and PM2.5 emission characteristics of Chinese multi-cuisines

Catering enterprises gather in densely populated areas, whose pollutant emission has an important impact on human health and environmental quality. This study investigated detailed information of 5456 catering enterprises in a typical city, and analyzed PM2.5 and VOCs components of 15 catering cuisines and different scales. Based on the measured emission factors, the basic information of nationwide catering enterprises and the population density, this study established the national catering pollutant emission inventory with the spatial resolution of 0.1° × 0.1°. As the results showed, alkanes, alkenes and oxygenated volatile organic compounds (OVOCs) were the main components in VOCs of most catering cuisines, accounting for 36.6%–96.7%, while organic carbon (OC) was the dominant component in PM2.5, accounting for 55.4%–68.6%. The same cuisine of different scales also presented certain differences in emission characteristics. The total emissions of the ozone formation potential (OFP) and secondary organic aerosols formation potential (SOAFP) were 1737.8 kt and 76.4 kt, respectively, with alkanes and OVOCs being the main contributors, contributing 27.5% and 22.3%, respectively. Acetaldehyde, ethylene, propylene and the other top ten VOC species contribute 81.0% of the total OFP emissions, while toluene contributed much more to total SOAFP emissions than other VOC species, contributing 35.9%. The air pollutant emissions showed the highest correlation with the population, and the regions with prominent emissions were mainly concentrated in the Beijing-Tianjin-Hebei (BTH) region and coastal cities. This study is a systematic understanding of air pollutant emission characteristics of the catering industry in China, which has important supporting value for pollutant emission reduction planning in the catering industry.

A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks

Top-down approaches, such as atmospheric inversions, are a promising tool for evaluating emission estimates based on activity-data. In particular, there is a need to examine carbon budgets at subnational scales (e.g. state/province), since this is where the climate mitigation policies occur. In this study, the subnational scale anthropogenic CO2 emissions are estimated using a high-resolution global CO2 inverse model. The approach is distinctive with the use of continuous atmospheric measurements from regional/urban networks along with background monitoring data for the period 2015–2019 in global inversion. The measurements from several urban areas of the U.S., Europe and Japan, together with recent high-resolution emission inventories and data-driven flux datasets were utilized to estimate the fossil emissions across the urban areas of the world. By jointly optimizing fossil fuel and natural fluxes, the model is able to contribute additional information to the evaluation of province–scale emissions, provided that sufficient regional network observations are available. The fossil CO2 emission estimates over the U.S. states such as Indiana, Massachusetts, Connecticut, New York, Virginia and Maryland were found to have a reasonable agreement with the Environmental Protection Agency (EPA) inventory, and the model corrects the emissions substantially towards the EPA estimates for California and Indiana. The emission estimates over the United Kingdom, France and Germany are comparable with the regional inventory TNO–CAMS. We evaluated model estimates using independent aircraft observations, while comparison with the CarbonTracker model fluxes confirms ability to represent the biospheric fluxes. This study highlights the potential of the newly developed inverse modeling system to utilize the atmospheric data collected from the regional networks and other observation platforms for further enhancing the ability to perform top-down carbon budget assessment at subnational scales and support the monitoring and mitigation of greenhouse gas emissions.

High-resolution emission inventory of biogenic volatile organic compounds for rapidly urbanizing areas: A case of Shenzhen megacity, China

The effects of volatile organic compounds on urban air quality and the ozone have been widely acknowledged, and the contributions of relevant biogenic sources are currently receiving rising attentions. However, inventories of biogenic volatile organic compounds (BVOCs) are in fact limited for the environmental management of megacities. In this study, we provided an estimation of BVOC emissions and their spatial characteristics in a typical urbanized area, Shenzhen megacity, China, based on an in-depth vegetation investigation and using remote sensing data. The total BVOC emission in Shenzhen in 2019 was estimated to be 3.84 × 109 g C, of which isoprene contributed to about 24.4%, monoterpenes about 44.4%, sesquiterpenes about 1.9%, and other VOCs (OVOCs) about 29.3%. Metropolitan BVOC emissions exhibited a seasonal pattern with a peak in July and a decline in January. They were mainly derived from the less built-up areas (88.9% of BVOC emissions). Estimated BVOCs comprised around 5.2% of the total municipal VOC emissions in 2019. This percentage may increase as more green spaces emerge and anthropogenic emissions decrease in built-up areas. Furthermore, synergistic effects existed between BVOC emissions and relevant vegetation-based ecosystem services (e.g., air purification, carbon fixation). Greening during urban sprawl should be based on a trade-off between BVOC emissions and ecosystem benefits of urban green spaces. The results suggested that urban greening in Shenzhen, and like other cities as well, need to account for BVOC contributions to ozone. Meanwhile, greening cites should adopt proactive environmental management by using plant species with low BVOC emissions to maintain urban ecosystem services while avoid further degradation to ozone pollution.

Maritime greenhouse gas emission estimation and forecasting through AIS data analytics: a case study of Tianjin port in the context of sustainable development

The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union’s incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port—a region characterized by high shipping activity—this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under various operational conditions. Coupled with the prediction results, the study offers valuable theoretical and data-driven support for formulating emission reduction strategies and optimizing resource allocation, thereby contributing to sustainable maritime transformation.

Methane and nitrous oxide emissions from municipal wastewater treatment plants in China: A plant-level and technology-specific study

Wastewater treatment is an important source of greenhouse gases (GHGs). Yet large uncertainties remain in the quantification of GHG emissions from municipal wastewater treatment plants (MWWTPs) in China. A high-resolution and technology-specific emission inventory is still lacking to support mitigation strategies of MWWTPs. Here we develop a plant-level and technology-based MWWTP emission inventory for China covering 8703 plants and 19 treatment technology categories by compiling and harmonizing the most up-to-date facility-level databases. China's methane (CH4) and nitrous oxide (N2O) emissions from MWWTPs in 2020 are estimated to be 150.6 Gg and 22.0 Gg, respectively, with the uncertainty range of −30% to 37% and −30% to 26% at 95% confidence interval. We find an emission inequality across cities, with the richest cities emitting two times more CH4 and N2O per capita from municipal wastewater treatment than the poorest cities. The emitted CH4 and N2O are dominated by Anaerobic/Anoxic/Oxic-, Sequencing Batch Reactor-, Oxidation Ditch-, and Anoxic/Oxic-based MWWTPs of less than 20 years old. Considering the relatively young age structure of China's MWWTPs, the committed emissions highlight the importance of reducing on-site GHG emissions by optimization of operating conditions and innovation management. The emission differences among our estimates, previous studies, and the Intergovernmental Panel on Climate Change guidelines are largely attributed to the uncertainties in emission factors, implying the urgent need for more plant-integrated measurements to improve the accuracy of emission accounting.

High-resolution emission inventory of full-volatility organic compounds from cooking in China during 2015–2021

Abstract. Quantifying the full-volatility organic emissions from cooking sources is important for understanding the causes of organic aerosol pollution. However, existing national cooking emission inventories in China fail to cover full-volatility organics and have large biases in estimating emissions and their spatial distribution. Here, we develop the first emission inventory of full-volatility organics from cooking in China, which covers emissions from individual commercial restaurants as well as residential kitchens and canteens. In our emission estimates, we use cuisine-specific full-volatility emission factors and provincial policy-driven purification facility installation proportions, which allows us to consider the significant impact of diverse dietary preferences and policy changes on China's cooking emissions. The 2021 emissions of volatile organic compounds (VOCs), intermediate-volatility organic compounds (IVOCs), semi-volatile organic compounds (SVOCs), and organic compounds with even lower volatility (xLVOCs) from cooking in China are 561 (317–891, 95 % confidence interval) kt yr−1, 241 (135–374) kt yr−1, 176 (95.8–290) kt yr−1, and 13.1 (7.36–21.0) kt yr−1, respectively. The IVOC and SVOC emissions from cooking account for 9 %–21 % and 31 %–62 % of the total emissions from all sources in the five most densely populated cities in China. Among all cooking types, commercial cooking dominates the emissions, contributing 54.5 %, 66.2 %, 68.5 %, and 46.7 % to the VOC, IVOC, SVOC, and xLVOC emissions, respectively. Sichuan–Hunan cuisine contributes the most to total cooking emissions among all commercial cuisines. Residential cooking emissions are also important, accounting for 22.2 %–47.1 % of the cooking organic emissions across the four volatility ranges, whereas canteens make minor contributions to each volatility range (<10 %). In terms of spatial distribution, emission hotspots mainly occur in densely populated areas and regions with oily and spicy dietary preferences. From 2015 to 2021, national organic emissions from cooking increased by 25.2 % because of the rapid growth of the catering industry, despite being partly offset by the increased installation of purification facilities. Future control measures need to further promote the purification facilities in commercial restaurants and improve their removal efficiency as well as reduce emissions from residential cooking. Our dataset and generalizable methodology serve as valuable resources for evaluating the air quality, climate, and health impacts of cooking sources, and help to formulate effective emission control policies. Our national, multi-year, high-spatial-resolution dataset can be accessed from https://doi.org/10.6084/m9.figshare.23537673 (Li et al., 2023).

Towards sustainable transport: quantifying and mitigating pollutant emissions from heavy-duty diesel trucks in Northeast China.

Heavy-duty diesel trucks (HDDTs) have caused serious environmental pollution in China. Accurate estimation of their pollutant emission characteristics is essential to reduce emissions and associated environmental and public health impacts. To achieve sustainable development for transport emissions in Northeast China, we developed localized emission factors and a high-resolution emission inventory of HDDTs, based on on-board test, Guidebook and international vehicle emission (IVE) model. The results show that the total emissions of CO, NO, NO2, and PM from HDDTs in Northeast China in 2020 were 172.2 kt, 531.5 kt, 11.2 kt, and 921.4 t, respectively. In terms of spatial distribution, emissions decreased from the city center to the city fringe. Temporally, the NOx emission variation curves of different types of roads presented a "single-peak" emission characteristic, which was different from the peak of traffic flow. Three emission reduction scenarios are further developed in the paper. Scenario analysis shows that elimination of HDDTs that follow the old China III emission standard and installing tailpipe treatment devices are the most effective pollutant reduction measure. The reduction percentages for CO, NO, NO2, and PM ranged from 62.9 to 83.89%. The results of our study could inform policymakers to devise feasible strategies to reduce vehicle pollution in Northeast China.

Evaluating the Ability of the Pre-Launch TanSat-2 Satellite to Quantify Urban CO2 Emissions

TanSat-2, the next-generation Chinese greenhouse gas monitoring satellite for measuring carbon dioxide (CO2), has a new city-scale observing mode. We assess the theoretical capability of TanSat-2 to quantify integrated urban CO2 emissions over the cities of Beijing, Jinan, Los Angeles, and Paris. A high-resolution emission inventory and a column-averaged CO2 (XCO2) transport model are used to build an urban CO2 inversion system. We design a series of numerical experiments describing this observing system to evaluate the impacts of sampling patterns and XCO2 measurement errors on inferring urban CO2 emissions. We find that the correction in systematic and random flux errors is correlated with the signal-to-noise ratio of satellite measurements. The reduction in systematic flux errors for the four cities are sizable, but are subject to unbiased satellite sampling and favorable meteorological conditions (i.e., less cloud cover and lower wind speed). The corresponding correction to the random flux error is 19–28%. Even though clear-sky satellite data from TanSat-2 have the potential to reduce flux errors for cities with high CO2 emissions, quantifying urban emissions by satellite-based measurements is subject to additional limitations and uncertainties.

An exploratory approach to estimate point emission sources

The current atmospheric emission inventories do not fully meet the spatial and temporal resolution requirements of air quality modelling applications. Considering Portugal as a case study and focusing on combustion point emission sources (i.e., public power, refineries, manufacturers, and construction activities), this work proposes a methodological approach and dataset to estimate anthropogenic emissions suitable for different spatial scales (from regional to local). The obtained results were similar to the annual values reported by the Portuguese Environment Agency with the maximum emissions being estimated for manufacturing and construction activities. No significant differences were recorded between the temporal profiles developed in this and previous studies. However, the country-specific proxies from the developed database allowed us to better represent the temporal and spatial patterns of the Portuguese atmospheric emissions. The combination of the BigAir database with a comprehensive and standardized approach could help policymakers define mitigation and/or plan measures to reduce emissions from point sources, support countries worldwide (with a lack of data) to develop high-resolution emission inventories, and improve the current global and European inventories.

Mapping high-resolution energy consumption CO2 emissions in China by integrating nighttime lights and point source locations

High-resolution CO2 emission inventories are essential to accurately assess spatiotemporal patterns of carbon emissions, analyze factors affecting carbon emissions, and develop sound emission reduction policies. The top-down approach is often used to map CO2 emissions from energy consumption due to its simplicity. However, the spatial proxy variables commonly used in this method, such as nighttime light (NL), land use, and population, are difficult to reflect the spatial distribution of CO2 emissions from large point sources. Therefore, this study uses the active fire product provided by Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite to extract the location of industrial heat sources in China, and then develops an improved CO2 emission estimation model by integrating industrial heat sources, Global Energy Monitor (GEM) power plant location and nighttime lights. The model is used to map CO2 emissions from energy consumption at a resolution of 1 km*1 km from 2012 to 2019 in China. It is found that the overall accuracy of the model is greatly improved at the provincial level, the R2 value is >0.75, and RMSE is distributed in 40–110 Mt. At the grid level, the improved model allocates more carbon emissions to the grid where the point source is located, which makes the spatial distribution of CO2 emissions more reasonable.

An improved hourly-resolved atmospheric NOx emission inventory of industrial sources based on Continuous Emission Monitoring System data: Case of Jiangsu Province, China

Current emission inventories with low spatial and temporal resolutions, slow updates, and great uncertainty can no longer meet the new demands for the precise prevention and control of air pollution. This study considered industrial sources of Jiangsu province in 2018 as the research object and divided key industrial sources into 16 processes. Based on the continuous emission monitoring system (CEMS) data of key enterprises, millions of hourly scale monitoring data from 17842-point source enterprises at 169289 emission outlets were collected. An hourly scale high-resolution industrial source NOx emission inventory was constructed and compared with existing inventories. The total NOx emissions of power plants, industrial boilers, ferrous metal manufacturing, non-metallic mineral manufacturing, and chemical manufacturing industries in Jiangsu Province in 2018 were 55, 27, 64, 28, and 3 Gg, respectively. The total emissions from industrial sources were high in summer, reaching a peak of 17 Gg in July, and low in winter, reaching 11 Gg in February. The emission factors of the power plants, coking, and cement industries decreased by 15.95%, 29.03%, and 51.61%, respectively. Hourly scales showed that the power plants had the most considerable fluctuation in 24 h emissions at 5.94%, with high emissions occurring in the afternoon and at night. However, the 24 h emissions of ferrous metal manufacturing fluctuated slightly, at only 3.22%, and the high emissions mostly occurred at night. The WRF-Chem model was used for simulation validation. The NO2 simulation results based on this study's inventory were significantly better than those based on the Multi-resolution Emission Inventory for China (MEIC), with normalized mean biases (NMBs) of −7.1% and −10.7% for January and July, respectively, as opposed to 30.8% and 14.4% in the MEIC. The hourly scale high-precision emission inventory established in this study is significant for formulating real-time differentiated precise prevention and control policies and improving the accuracy of air quality models.

High-resolution atmospheric mercury emission from open biomass burning in China: Integration of localized emission factors and multi-source finer resolution remote sensing data

Mercury (Hg) emissions from open biomass burning represent one of the largest Hg inputs to the atmosphere, with considerable effects on the atmospheric Hg budget. However, there is currently large uncertainty in the inventory of Hg emissions from open biomass burning in China due to limitations on the coarse resolution of burned area products, rough biomass data, and the unavailability of suitable emission factors (EFs). In this study, we developed high tempo-spatial resolution (30 m) and long time-series (2000–2019) atmospheric Hg emission inventories from open biomass burning using the Global Annual Burned Area Map (GABAM) product, high-resolution biomass map, Landsat-based tree cover datasets as well as local EFs in China. The results showed that the average annual Hg emission from open biomass burning in China amounted to 172.6 kg during 2000–2019, with a range of 63–398.5 kg. The largest Hg emissions were found in cropland (72%), followed by forest (25.9%), and grassland (2.1%). On a regional level, Northeast China (NE) and Southwest China (SW) were the two main contributors, together accounting for more than 60% of total Hg emissions. The temporal distribution of Hg emissions showed that the peaks occurred in 2003 and 2014. This is a comprehensive estimation of Hg emissions from open biomass burning in China by integrating various high-resolution remotely sensed data and nationwide localized EFs, which has important implications for understanding the role of open biomass burning in China in regional and global atmospheric Hg budget.

High-resolution downscaling of source resolved PM2.5 predictions using machine learning models

Accurate predictions of source resolved atmospheric PM2.5 concentrations at high resolutions using chemical transport models (CTMs) require expensive CTM simulations and development of high-resolution emissions inventories. We use multiple machine learning (ML) approaches to downscale coarse-resolution (36 × 36 km2) CTM predictions to 1 × 1 km2 spatial resolution. ML predictions include concentrations of the major chemical components of PM2.5 and the contributions of its major emissions sources. Inputs for the ML models include 36 × 36 km2 source resolved CTM predicted concentrations of all PM2.5 components, meteorological data, and several land-use (LU) variables. The output of our ML models is the 1 × 1 km2 source-resolved concentrations of all major PM2.5 components in southwestern Pennsylvania (5184 km2 domain) during February and July 2017. Models were trained and validated using 1 × 1 km2 resolution source- and species-resolved CTM predictions of PM2.5 from recent complementary studies. The best overall performance was found using a random forest (RF) model, where species and source resolved PM2.5 concentrations were reproduced with low normalized mean bias (|NMB| < 0.01). The downscaling model captures the spatial distribution of PM2.5 both by component and source, with some discrepancies when predicting the plumes of large point sources that have long-range impacts. In a test of generalizability to unknown domains, the model differentiates well between areas that are primarily urban, rural, or industrial but faces challenges with the reproduction of the effects of large point sources of PM2.5 when entire quadrants are removed from the training data. The results represent a proof of concept for downscaling low-resolution CTM predictions using native high-resolution CTM predictions in training.