Source-receptor Relationships Research Articles

Significant shift of footprint patterns and pollutant source contributions: insights from observations at Shanghuang observatory, East China

Abstract As two of the most important products of the combustion process, carbon dioxide (CO2) and carbon monoxide (CO) are commonly used as tracers for combustion source assignment. Their relationship will help to better understand the regional carbon cycle and assess climate forcing effects. In this study, mixing ratios of CO2 and CO were continuously measured using a Picarro gas concentration analyzer at the Atmospheric Boundary Layer Eco-Environmental Shanghuang Observatory, Chinese Academy of Sciences (ABLECAS) throughout 2022–2023. The variability of the mixing ratio of CO to CO2 (ΔCO/ΔCO2) in a 1 h time interval was calculated based on linear slope analysis after background values were determined and subtracted. The results showed that the mixing ratio of CO had a clear seasonal variability with a moderate increase in the spring (249.1 ± 59.6 part per billion (ppb)) and winter (257.8 ± 90.3 ppb), mostly due to more frequent transport from north of the Yangtze River. ΔCO/ΔCO2 at the ABLECAS varied with air mass origin, with a linear slope 0%–1% on a 1 h basis. Relatively high ΔCO/ΔCO2 values for an air mass from the north in the winter indicate that the emission sources had lower combustion efficiency. In summer, the ΔCO/ΔCO2 ratio mostly reflected the background conditions for air masses from marine areas. The potential source regions and contribution assignments were evaluatedat the ABLECAS according to source–receptor relationship analysis using the FLEXPART model with CO as a pollutant tracer from 2015 to 2023. We found that the footprint of an air mass had a clear transition period between 2018 and 2019, and a synoptic anomaly, related to Arctic Oscillation strength and west Pacific subtropical high position, plays a key role in influencing the pollutant transport patterns. This study provides a scientific basis for the formulation of air quality regulation policy, and helps to implement the national carbon neutralization strategy.

Inversing NOx emissions based on an optimization model that combines a source-receptor relationship correction matrix and monitoring data: A case study in Linyi, China

Inversion based on observational data and the source-receptor relationship (SRR) simulated by an air quality model is an effective means to estimate NOx emissions. However, the SRR bias induced by the inherent uncertainty of the simulated model leads to potential errors in the inversed emissions, but this is seldom considered in NOx inversion. In this study, we constructed an inversion model based on the SRR correction (IMSC) by introducing a correction matrix, combined with joint regularization scheme and genetic algorithm. We innovated the dynamic acquisition method of center-restricted parameters for the correction matrix, combined this with other parts of the IMSC, attaining the multi-month and multi-region pollutant emission inversion. Hypothetical examples demonstrated that the IMSC effectively corrected the SRR and accurately estimated the NOx emissions. The IMSC was used to estimate Linyi county-level NOx emissions for January, April, July and October (typical months) during 2020 and 2021. An inversion model without SRR correction (IMWSC) was developed for comparison with the IMSC. Results showed that the IMSC more accurately, robustly, and reasonably estimated NOx emissions. Compared to the IMWSC, the IMSC improved the mean correlation between NOx emissions and NO2 observational concentrations by 25.0%, enhancing the correlation between NOx emissions and NO2 column concentrations by 111.3%. The similar NOx emission change ratios (σaverage = 5.1%) between the typical months in 2021 and 2020 among the different counties indicated a more robust performance of the IMSC than the IMWSC (σaverage = 55.2%). In addition, the NOx emissions inversed by the IMSC also showed better consistency with social activity levels (i.e., electricity consumption). The typical monthly Linyi's county-level NOx emission characterization was also studied. NOx emissions were lower in April, July, and October 2021 than the same period in 2020 due to COVID-19 and pollution controls. This study provides strategies for swiftly and accurately estimating pollutant emissions.

Monthly Characteristics and Source–Receptor Relationships of Anthropogenic Total Nitrate in Northeast Asia

The complex nonlinear characteristics of atmospheric chemistry necessitate the development of new methods for calculating source–receptor (S–R) relationships for secondary air pollutants. In this study, the monthly characteristics and S–R relationships of anthropogenic total nitrate (i.e., the sum of N from nitric acid, inorganic nitrate, and peroxyacetyl nitrate) in Northeast Asia were simulated and analyzed. The Community Multiscale Air Quality (CMAQ), Fifth-Generation NCAR/Penn State Mesoscale (MM5), and Sparse Matrix Operator Kernel Emissions (SMOKE) models were employed for air quality modeling, meteorological fields, and emissions processing, respectively. The study area encompassed Republic of Korea, Japan, and most of China. Five source/receptor regions were defined to derive the S–R relationships: three in China, one in Republic of Korea, and one in Japan. To produce data for the calculation of the S–R relationship, several experiments were conducted with a 20% reduction in NOx emission sources. As a result of the S–R relationships, China was rarely impacted by the other two countries. The total depositions in other countries were significantly dominated by China (i.e., 43.5% and 40.7% in Republic of Korea and Japan, respectively, and up to 82.3% in December for Republic of Korea).

Decreasing trends in PM2.5 and BC concentrations observed on central and southwestern Japanese Islands

The interannual trends in fine particulate matter (PM2.5) and black carbon (BC) concentrations at remote stations in central (Noto) and southwestern (Fukue) Japan were investigated using statistical trend analysis and the chemical transport model, Regional Air Quality Model 2 (RAQM2ver3). The concentrations of PM2.5 and BC in Noto and Fukue exhibited seasonal variations and decreased from 2013 to 2020. Higher concentrations of PM2.5 and BC were observed during the spring season (April) in both locations. The PM2.5 concentrations peaked in Fukue approximately one month earlier than in Noto, while the BC concentration peak in Noto occurred one month earlier than that of PM2.5. The total reductions in PM2.5 concentrations in Noto and Fukue were 7.5 ± 5.0 and 3.8 ± 0.95 μg m−3, corresponding to reduction rates 1.1 ± 0.7 and 0.5 ± 0.95 μg m−3year−1, respectively, compared to the concentrations in 2013. The total reduction in BC concentrations in Noto from 2013 to 2019 was estimated to be 0.13 ± 0.1 μg m−3, amounting to 0.018 ± 0.014 μg m−3year−1 reduction rate. The results of the source–receptor relationships analysis suggest that the decreases in PM2.5 and BC concentrations in Fukue and Noto were significantly influenced by reductions in amounts transported from central China (CCHN, 30–40°N), northern China (NCHN, >40°N) and Japan (JPN) after 2015/2016. The decrease in emissions from these three regions accounted for the observed reductions in particulate concentrations.

Modeling Global Environmental Fate and Quantifying Global Source-Receptor Relationships of Short-, Medium-, and Long-Chain Chlorinated Paraffins.

Decades-long emissions and long-range transport of chlorinated paraffins (CPs) have resulted in their pervasive presence in the global environment. The lack of an understanding of the global distribution of short-, medium-, and long-chain CPs (SCCPs, MCCPs, and LCCPs) hinders us from quantitatively tracing their origins in remote regions. Using the BETR-Global model and historical emission estimates, we simulate the global dispersion of CPs from 1930 to 2020. Whereas contamination trends in the main contaminated regions (East Asia, Europe, North America, and South Asia) diverge, CP concentrations in the Arctic, Antarctica, and the Tibetan Plateau all increase. By 2020, East Asian, European, and North American emissions contributed 38%, 26%, and 18% of CP contamination in the High Arctic, respectively, while Southern hemispheric emissions and emissions around the Tibetan Plateau primarily contribute to CP contamination in central Antarctica and on the Plateau, respectively. Our results emphasize the important contribution of (i) European and North American emissions to historical CP contamination in remote regions and current MCCP and LCCP contamination in the High Arctic and (ii) East Asian emission to current SCCP and MCCP contamination of all three remote regions. These results can help to evaluate the effectiveness of potential global and regional CP emission-reduction strategies.

Global trade-driven transfer of atmospheric polycyclic aromatic hydrocarbon emissions and associated human inhalation exposure risk

Polycyclic aromatic hydrocarbons (PAHs) are of significant public concern because of their toxicity and long-range transport potential. Extensive studies have been conducted to explore the source-receptor relationships of PAHs via atmospheric transport. However, the transfer of trade-driven regional and global PAHs is poorly understood. This study estimated the virtual PAHs emission transfer embodied in global trade from 2004 to 2014 and simulated the impact of international trade on global contamination and associated human inhalation exposure risk of PAHs. Results show that trade-driven PAHs flowed primarily from developed to less-developed regions, particularly in those regions with intensive heavy industries and transportation. As the result, international trade resulted in an increasing risk of lung cancer induced by exposure to PAHs (27.8% in China, 14.7% in India, and 11.3% in Southeast Asia). In contrast, we found decreasing risks of PAHs-induced lung cancer in Western Europe (63.2%) and the United States (45.9%) in 2004. Our findings indicate that final demand and emission intensity are the key driving factors contributing to rising and falling consumption-based PAHs emissions and related health risk respectively. The results could provide a useful reference for global collaboration in the reduction of PAHs pollution and related health risks.

Optimizing emission control strategies for mitigating PM2.5 and O3 pollution: A case study in the Yangtze River Delta region of eastern China

Effective mitigation of fine particulate matter (PM2.5) and ozone (O3) pollution necessitates collaborative regional emission control of major pollutants. In this study, we propose an approach based on source-receptor relationships (SRRs) and a mathematical programming (MP) model to quantify the regional atmospheric environmental capacity (AEC) constrained by certain air quality goals for PM2.5 and O3. We apply this method to optimize emission control strategies addressing a springtime case of concurrent high PM2.5 and O3 pollution in the Yangtze River Delta (YRD) region of eastern China in May 2014. Our analysis of SRRs reveals that O3 pollution is more contributed by regional transport compared to PM2.5, thereby limiting the potential for meeting the O3 target through anthropogenic emission reductions. Imposing various constraints on air quality goals and upper limits of emission reduction ratios yields significant variations in potential control pathways specific to source sectors and regions. To effectively mitigate PM2.5 pollution, substantial reductions in sulfur dioxide (SO2) and primary PM2.5 emissions are required, whereas ammonia (NH3) control demonstrates less effectiveness. Differentiated and coordinated efforts in controlling nitrogen oxide (NOx) and volatile organic compounds (VOCs) emissions are necessary to simultaneously achieve the desired PM2.5 and O3 targets. Evaluation of potential control pathways further emphasizes the effectiveness of implementing control measures on major precursor emissions to reduce PM2.5. However, meeting the O3 target remains challenging due to the complex nonlinearity involved in O3 formation. To attain the air quality goals for PM2.5 (<50 μg m−3) and daily maximum 1-h average (MDA1) O3 (<160 μg m−3) across the entire YRD, the estimated AEC values for SO2, NOx, NH3, VOCs, and primary PM2.5 are approximately 8.3, 79.4, 102.7, 186.9, and 13.0 kt mon−1, respectively, with corresponding emission reduction ratios of 95.6%, 75.5%, 33.1%, 45.1%, and 85.5% at the regional level.

Source-Receptor Relationships Between Precursor Emissions and O3 and PM2.5 Air Pollution Impacts.

Reduced complexity tools that provide a representation of both primarily emitted particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), secondarily formed PM2.5, and ozone (O3) allow for a quick assessment of many iterations of pollution control scenarios. Here, a new reduced complexity tool, Pattern Constructed Air Pollution Surfaces (PCAPS), that estimates annual average PM2.5 and seasonal average maximum daily average 8 h (MDA8) O3 for any source location in the United States is described and evaluated. Typically, reduced complexity tools are not evaluated for skill in predicting change in air pollution by comparison with more sophisticated modeling systems. Here, PCAPS was compared against multiple types of emission control scenarios predicted with state-of-the-science photochemical grid models to provide confidence that the model is realistically capturing the change in air pollution due to changing emissions. PCAPS was also applied with all anthropogenic emissions sources for multiple retrospective years to predict PM2.5 chemical components for comparison against routine surface measurements. PCAPS predicted similar magnitudes and regional variations in spatial gradients of measured chemical components of PM2.5. Model performance for capturing ambient measurements was consistent with other reduced complexity tools. PCAPS also did well at capturing the magnitude and spatial features of changes predicted by photochemical transport models for multiple emissions scenarios for both O3 and PM2.5. PCAPS is a flexible tool that provides source-receptor relationships using patterns of air quality gradients from a training data set of generic modeled sources to create interpolated air pollution gradients for new locations not part of the training database. The flexibility provided for both sources and receptors makes this tool ideal for integration into larger frameworks that provide emissions changes and need estimates of air quality to inform downstream analytics, which often includes an estimate of monetized health effects.

Size-resolved inhalation intake fractions for particles released from human activities in residential indoor environments

Inhalation exposure to elevated concentrations of airborne particulate matter is a public health concern. Assessment of exposure can be enhanced through better knowledge of source-receptor relationships, which can be characterized through the inhalation intake fraction metric. This case study provides new insights on variations in particle inhalation intake fractions for indoor sources associated with common human activities in residential buildings. In a controlled climate chamber (air temperature: 24 ± 1 °C, relative humidity: 50 ± 5%), we investigated size-resolved intake fractions for particles in relation to four scripted activities performed by a human volunteer: sitting, walking, cooking, and vacuuming. We measured size- and time-resolved particle number concentrations at the volunteer's breathing zone to characterize intake fractions. In addition, we measured particles at four different stationary locations across the climate chamber to assess the degree of spatial heterogeneity in particulate matter concentrations. The results show that particles released from human skin and clothing during sitting were associated with the highest total inhalation intake fraction (13‰), followed by cooking (9‰), vacuuming (5.7‰), and walking (3.9‰). These results highlight how breathing zone proximity to localized emission sources and low indoor air mixing can enhance inhalation exposure to particles. Sitting and cooking caused a maximum inhalation intake fraction in the size range of 1–3 μm. Findings also show that the assumption of a perfectly mixed environment could lead to an underestimation of the inhalation intake of particles by up to 3.2-fold. The results of this case study provide a basis for achieving more accurate personal inhalation exposure assessment and improved indoor air quality management.

Identifying the impacts of warming anomalies in the Arctic region and the Tibetan Plateau on PM2.5 pollution and regional transport over China

Arctic region (AR) and Tibetan plateau (TP) are the most sensitive regions to climate change and have synchronously experienced accelerated warming over recent decades. Based on the multi-year observational data of PM2.5 and meteorology, we investigated the modulation of AR and TP's anomalous warming on the variations of PM2.5 pollution and regional transport over central and eastern China (CEC), including major PM2.5 pollution regions in northern China (NC), the Yangtze river delta (YRD), central China (CC), and southern China (SC). Results show that the thermal effects of TP and AR modulated PM2.5 pollution in different ways respectively with prominently influencing the interannual changes of PM2.5 concentrations and heavy PM2.5 pollution occurrences. The impacts of anomalous warming in TP and AR on PM2.5 pollution consistently enhanced regional PM2.5 transport from NC to YRD and SC, while the warming anomalies over AR and TP respectively enhanced and inhibited the southward transport of PM2.5 from NC. In association with the synergistic impact of warming anomalies in AR and TP, the regional PM2.5 transport over CEC presented the inverse patterns of the northward anomalies between NC and CC and the southward anomalies from the eastern part of NC, YRD to SC, forming the regional distribution with negative PM2.5 anomalies in NC and CC and positive PM2.5 anomalies in YRD and SC. Our results highlight the effect of warming anomalies in AR and TP on the PM2.5 pollution over China with the implication of source–receptor relationship of regional transport of air pollutants.

Adjusting elemental carbon emissions in Northeast Asia using observed surface concentrations of downwind area and simulated contributions

In this study, we developed a practical approach to augment elemental carbon (EC) emissions to improve the reproducibility of the most recent air quality with photochemical grid modeling in support of source-receptor relationship analysis. We demonstrated the usefulness of this approach with a series of simulations for EC concentrations over Northeast Asia during the 2016 Korea-United States Air Quality study. Considering the difficulty of acquiring EC observational data in foreign countries, our approach takes two steps: (1) augmenting upwind EC emissions based on simulated upwind contributions and observational data at a downwind EC monitor considered as the most representative monitor for upwind influences and (2) adjusting downwind EC emissions based on simulated downwind contributions, including the effects of updated upwind emissions from the first step and observational data at the downwind EC monitors. The emission adjustment approach resulted in EC emissions 2.5 times higher than the original emissions in the modeling domain. The EC concentration in the downwind area was observed to be 1.0 μg m−3 during the study period, while the simulated EC concentration was 0.5 μg m−3 before the emission adjustment. After the adjustment, the normalized mean error of the daily mean EC concentration decreased from 48 % to 22 % at ground monitor locations. We found that the EC simulation results were improved at high altitudes, and the contribution of the upwind areas was greater than that of the downwind areas for EC concentrations downwind with or without emission adjustment. This implies that collaborating with upwind regions is essential to alleviate high EC concentrations in downwind areas. The developed emission adjustment approach can be used for any upwind or downwind area when transboundary air pollution mitigation is needed because it provides better reproducibility of the most recent air quality through modeling with improved emission data.

Response of Arctic Black Carbon Contamination and Climate Forcing to Global Supply Chain Relocation.

Black carbon (BC) plays a vital role in Arctic warming. Extensive investigations have been conducted to elucidate the source-receptor relationships of BC between the Arctic and mid-/high-latitude sources. However, it is unclear to what extent source relocation under globalization could disturb Arctic BC contamination and climate forcing from anthropogenic BC emissions. Here, we show that the global supply chain (GSC) relocation featured by the southward shift of industries from high-latitude developed countries to low-latitude developing countries markedly reduces the BC burden in the Arctic using a global chemical transport model (GEOS-Chem) and a multiregional input-output analysis (MRIO). We find that Arctic annual mean BC concentration associated with the GSC relocation drops by ∼15% from the case without the GSC relocation. The total net BC level declines 7% over the entire Arctic and 16% in the European Arctic. We also observed markedly declining BC deposition as well as direct and snow albedo radiative forcing in the Arctic. We show that the Arctic BC burden would be further reduced by decreasing BC emissions in China, attributable to its emission reduction and ongoing shift of the GSC from China to southern and southeastern Asia.

A fast method for ammonia emission reduction scenario studies: Combining EMEP4NL with the SHERPA tool

We present a fast method for calculating the effects of ammonia emission reduction scenarios on reduced nitrogen components over the Netherlands. The method combines the EMEP4NL model, which is a full atmospheric chemical transport model (ACTM), with the SHERPA tool, which is an approximate representation of a full ACTM. The SHERPA tool uses a geographically weighted regression method to derive source-receptor relations, based on a limited number of “training” runs of the ACTM (here, EMEP4NL). The training runs consist of a base run of the year of interest plus uniform emission reduction runs for the primary pollutants of interest. Once the source-receptor relations are derived, scenario studies can be performed at a much lower computational cost with SHERPA (i.e. order 104–105 more efficient) as compared to a full run with the EMEP4NL model. Here, we study five specific ammonia emission reduction scenarios, in which a 30% emission reduction is enforced for specific regions in The Netherlands, Germany and Belgium. Our analysis shows that the EMEP4NL model and the SHERPA tool yield similar results in terms of the reduction of the yearly average concentration and deposition of reduced nitrogen components. Because of its computational efficiency and ability to adequately reproduce the results of the EMEP4NL model, the SHERPA tool is relevant for policy making. It can provide predictions of the effects of many different local emission reduction measures in a computationally efficient manner.

A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME

Abstract. Lagrangian particle dispersion models (LPDMs) have been used extensively to calculate source-receptor relationships (“footprints”) for use in applications such as greenhouse gas (GHG) flux inversions. Because a single model simulation is required for each data point, LPDMs do not scale well to applications with large data sets such as flux inversions using satellite observations. Here, we develop a proof-of-concept machine learning emulator for LPDM footprints over a ∼ 350 km × 230 km region around an observation point, and test it for a range of in situ measurement sites from around the world. As opposed to previous approaches to footprint approximation, it does not require the interpolation or smoothing of footprints produced by the LPDM. Instead, the footprint is emulated entirely from meteorological inputs. This is achieved by independently emulating the footprint magnitude at each grid cell in the domain using gradient-boosted regression trees with a selection of meteorological variables as inputs. The emulator is trained based on footprints from the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) for 2014 and 2015, and the emulated footprints are evaluated against hourly NAME output from 2016 and 2020. When compared to CH4 concentration time series generated by NAME, we show that our emulator achieves a mean R-squared score of 0.69 across all sites investigated between 2016 and 2020. The emulator can predict a footprint in around 10 ms, compared to around 10 min for the 3D simulator. This simple and interpretable proof-of-concept emulator demonstrates the potential of machine learning for LPDM emulation.

Unexpected high contribution of in-cloud wet scavenging to nitrogen deposition induced by pumping effect of typhoon landfall in China

Atmospheric nitrogen deposition has large eco-environmental effects such as ocean acidification, eutrophication in coastal areas. However, knowledge of the source and the pathway of N deposition in coastal areas is limited, especially during tropical storms, hindering the accurate quantification of how anthropogenic activities influence the ocean ecosystem. In this study, the Nested Air Quality Prediction Modeling System was used to investigate the wet deposition of N induced by typhoon Hagupit over eastern coastal China from an in- and below-cloud process perspective. Our results reveal for the first time an enhancement mechanism of N deposition related to the ‘pumping effect’ of the typhoon. Different from the non-typhoon conditions, air pollutants in the typhoon-affected regions were pumped into the higher altitudes and deposited via the in-cloud scavenging process in the moving path of the typhoon-affected regions. This study updates our understanding of the source–receptor relationship on atmospheric wet deposition caused by tropical cyclones.

Exposure risk to carbon monoxide concentrations inside a diesel-based bus rapid transport system: a CFD-Monte Carlo modeling approach

Bus rapid transit (BRT) vehicles are common microenvironments in urban areas. In some cities, these BRT vehicles are diesel-powered, which makes them highly pollutant. Recent studies report high levels and exposure risk to particulate matter in BRT vehicles. Nevertheless, extensive research has yet to be published, including gaseous pollutants (e.g., CO). Nevertheless, extensive research including gaseous pollutants (e.g., CO) has not been published. This research aims to evaluate the self-pollution of BRT buses in terms of exhaust gasses. For this, measurements and computational fluid dynamics (CFD) were used. Results suggest that pollutant concentrations stay low during most of the trips. However, some areas of the buses have significant swings and peaks due to the transit cycle. Here, we used CFD modeling to evaluate the dispersion of the exhaust CO inside and outside the bus. CFD results show that the bus rear has the highest concentrations, with a mean self-pollution ratio of 12%. Additionally, we developed a method based on the source-receptor relationship to quantify the impact of exhaust emissions reduction on self-pollution, showing that the technological replacement of current diesel buses would reduce self-pollution and, therefore, passenger exposure. Finally, since modeling results may be inaccurate, an uncertainty analysis was developed using the Monte Carlo method to obtain a confidence interval of 90% for the variables linked to the self-pollution.

Health Risks of Major Air Pollutants, their Drivers and Mitigation Strategies: A Review

The impact of increasing air pollution on human health and the environment is a major concern worldwide. Exposure to air pollution is one of the leading risk factors and substantially contributes to morbidity and premature mortality. This review paper aims to examine the exposure of major air pollutants (i.e., particulate matter, sulfur dioxide, oxides of nitrogen, carbon monoxide) and its association with respiratory, cardiovascular, reproductive, and genotoxic adverse health outcomes that can cause DNA damage leading to genetic mutations. The study emphasized how a better understanding of source-receptor relationships and exposure assessment methodologies can support effective air quality management planning. Hence, there is a need to augment various exposure indicators (spatial modeling, personal/area monitoring, emphasizing central/rural site measurements, etc.) to generate reliable surrogates for informed decision-making. The critical drivers of anthropogenic interference for air pollution remain urbanization, growing vehicle use, and industrialization. This requires innovative approaches, such as energy-efficient and technologically sustainable solutions to gradually replace conventional fossil fuel from primary energy mix with renewable energy. It holds the key to meet future energy challenges and minimizing air pollution emissions. Further, there is an urgent need to frame effective public policy with graded mitigation actions to reduce the adverse impact of air pollution on human health and the environment.

Comprehensive Evaluation of Odor-Causing VOCs from the Painting Process of the Automobile Manufacturing Industry and Its Sustainable Management

Automotive manufacturing is one of the potential sources of air pollution particularly involving volatile organic compounds (VOCs). This study intensively evaluated VOC emissions and their dispersion from the industry. The measured VOCs were speciated for further evaluation of their odor threats according to the characteristics of each compound. Mathematical emission and air dispersion models were applied to assist in elaborating the source–receptor relationship allowing the determining of existing business-as-usual conditions with proposed mitigation measures to manage the pollution of the factory studied in this paper. Seven VOC species potentially caused odor problems to the surrounding community, including 1-butanol, ethyl benzene, toluene, m,p xylene, o xylene, methyl ethyl ketone, and methyl isobutyl ketone. The results from the AERMOD dispersion model revealed that the smell from these chemicals could reach up to about 800 m from the source. Analysis of mitigation measures indicated that two interesting scenarios should be considered according to their effectiveness. The concentrations of VOCs can decrease by up to 4.7, 14.0 and 24.9% from increasing the physical stack height by +1, +3 and +5 m from its existing height, respectively. Modification of the aeration tank of the wastewater treatment unit to a closed system also helped to reduce about 27.8% of emissions resulting in about a 27.6% decreased ambient air concentration. This study provided useful information on the characteristics of VOCs emitted by the automobile manufacturing industry. It also demonstrated the relevant procedures and highlights the necessity to comprehensively analyze the source–receptor relationship to evaluate the most appropriate measures in managing industrial air pollution.

Study of aerosol anomaly associated with large earthquakes ([formula omitted

We present the variation of unusual atmospheric phenomena, aerosols, to understand the preseismic irregularities for two major earthquakes in Japan. We consider aerosol optical depth and Angstrom exponent data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite to establish possible connections between earthquakes and the generation of aerosols. Variation of the aerosol parameters shows significant changes before the April 15, 2016, Kumamoto earthquake (M=7.0,h=10 km) and the November 21, 2016, Fukushima earthquake (M=6.9 and h=9 km), where M indicates the Richter magnitude and h indicates the focal depth. To identify the source of the aerosol particles, we use the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT-4). This model uses both Lagrangian and Eulerian approaches to compute trajectories and establish a source-receptor relationship. We compute backward trajectories to check whether the aerosol generated near the epicenter is due to the preseismic processes or is transported from other areas. From our results, we conclude the fine-mode aerosols are generated in the vicinity of the epicenter, 3–7 days before the earthquakes.

Effect of Vertical Wind Shear on PM2.5 Changes over a Receptor Region in Central China

Vertical wind shear (VWS) significantly impacts the vertical mixing of air pollutants and leads to changes in near-surface air pollutants. We focused on Changsha (CS) and Jingmen (JM), the upstream and downstream urban sites of a receptor region in central China, to explore the impact of VWS on surface PM2.5 changes using 5-year wintertime observations and simulations from 2016–2020. The surface PM2.5 concentration was lower in CS with higher anthropogenic PM2.5 emissions than in JM, and the correlation between wind speed and PM2.5 was negative for clean conditions and positive for polluted conditions in both two sites. The difference in the correlation pattern of surface PM2.5 and VWS between CS and JM might be due to the different influences of regional PM2.5 transport and boundary layer dynamics. In downstream CS, the weak wind and VWS in the height of 1–2 km stabilized the ABL under polluted conditions, and strong northerly wind accompanied by enhanced VWS above 2 km favored the long-range transport of air pollutants. In upstream JM, local circulation and long-range PM2.5 transport co-determined the positive correlation between VWS and PM2.5 concentrations. Prevailed northerly wind disrupted the local circulation and enhanced the surface PM2.5 concentrations under polluted conditions, which tend to be an indicator of regional transport of air pollutants. The potential contribution source maps calculated from WRF-FLEXPART simulations also confirmed the more significant contribution of regional PM2.5 transport to the PM2.5 pollution in upstream region JM. By comparing the vertical profiles of meteorological parameters for typical transport- and local-type pollution days, the northerly wind prevailed throughout the ABL with stronger wind speed and VWS in transport-type pollution days, favoring the vertical mixing of transported air pollutants, in sharp contrast to the weak wind conditions in local-type pollution days. This study provided the evidence that PM2.5 pollution in the Twain-Hu Basin was affected by long-distance transport with different features at upstream and downstream sites, improving the understanding of the air pollutant source–receptor relationship in air quality changes with regional transport of air pollutants.