Source Contribution Analysis Research Articles

Heterogeneity in the health effects of PM2.5 sources across the major metropolitan cities, South Korea: Significance of region-specific management

Ambient PM2.5, well-known for its adverse impacts on human health, is a very heterogeneous pollutant. Its chemical composition and attributable sources vary by region, influenced by meteorological and geographical conditions as well as emission sources. However, administrative policies are currently focused on mass concentrations. However, not all PM2.5 sources provide equally toxic particles. Thus, those sources that should be the focus of controls has not been the priority. In the present study, we conducted source apportionment utilizing positive matrix factorization (PMF) and investigated the association of PM2.5 source contributions with emergency department visits (EDVs) in major megacities in South Korea. Overall, an interquartile range (IQR) increment in source contribution increased the number of emergency room visits. Industry and coal combustion sources, marked by heavy metals, were principally associated with the adverse health impacts. However, the sources showing significant associations with EDVs differed across the study area. In addition, we found that region-specific relationships between PM2.5 sources and morbidity were plausible, considering the existence of relevant sources such as industrial complexes and coal-fired power plants. The analysis of source contributions according to wind conditions also supported the source-morbidity relationships. These findings suggest that administrative policies for PM2.5 control should be established and implemented considering region-specific characteristics of the links between PM2.5 sources and health impacts to maximize the control’s public health effects. Furthermore, the results of the present study indicate that PMF was an effective method for linking acute exposure to PM2.5 source types with health outcomes to prioritize its sources.

Variation Characteristics of PM2.5 Pollution and Transport in Typical Transport Channel Cities in Winter

Taking Handan, Xingtai, Hengshui, and Cangzhou, four cities in southwest Hebei Province along the Beijing-Tianjin-Hebei typical transport route, as examples, we analyzed the variation characteristics of 3a meteorological conditions and PM2.5 concentration in winter from 2019 to 2021 and used potential source contribution analysis (PSCF) and concentration weight analysis (CWT) to identify the transport characteristics of PM2.5 in the four cities during the study period. Based on the meteorological air quality model (WRF-CMAQ) transmission matrix method and transport flux method, the contribution of PM2.5 transport between Handan, Xingtai, Hengshui, and Cangzhou and the surrounding areas was quantitatively assessed; the vertical distribution characteristics of PM2.5 net transport flux were revealed; and the two main transport routes of PM2.5 pollution were further identified. The results showed that during the study period, the PM2.5 concentration decreased by 45.85%, 49.45%, 42.40%, and 31.65%, respectively. The potential source contribution of Handan and Xingtai was mainly distributed in south-central Shanxi (Linfen, Changzhi, and Jinzhong), northern Henan (Xinxiang, Kaifeng, and Zhengzhou), and a small part of Inner Mongolia (PSCF > 0.9). The potential contribution areas of Hengshui and Cangzhou were mainly concentrated in southern Hebei (Handan and Shijiazhuang), central Shanxi (Taiyuan and Yangquan), and some Shandong regions (PSCF > 0.7), and the CWT results were similar to those of PSCF. During the study period, the local contribution (51.11%-62.99%) was slightly higher than the regional contribution (37.01%-48.89%) during winter in the four cities. Affected by horizontal turbulence and vertical diffusion, the impact of regional transmission in 2020 was slightly higher than that in other years (0.50%-9.52%). In 2021, the influence of regional transmission was slightly lower than that of other years (-2.15%--9.52%) due to low PM2.5 concentration and meteorological factors. For Handan, Xingtai, Hengshui, and Cangzhou, the total inflow (outflow) flux intensity of the four cities during winter and the surrounding areas was in 2020 > 2021 > 2019. For the total net flux, the total inflow (outflow) flux intensity of the four cities in winter was 0.094, -0.070, and 0.087 kt·d-1 (Xingtai:0.212, 0.395, and 0.544 kt·d-1; Hengshui:-0.040, -0.228, and 0.185 kt·d-1; Cangzhou:0.062, 0.126, and 0.128 kt·d-1). During the study, Handan, Xingtai, and Cangzhou were mostly used as transport receptors, whereas Hengshui was mostly used as a transport source. In the range of 0-1 260 m, the net transport flux intensity of PM2.5 increased basically with the increase in height, and the maximum net flux of the various cities in different periods was different. The maximum net flux of Handan, Xingtai, and Hengshui was 252-1 261 m, 817 m, and 252-817 m, respectively. The maximum net flux in Cangzhou was 252-359 m. By analyzing the transmission characteristics of the four cities, it was found that there were two main transport directions of PM2.5, that is, the northwest-southeast direction (Shanxi → Handan → Henan and Shandong; Shijiazhuang → Xingtai → Handan and Shandong; Baoding → Cangzhou → Shandong) and the southwest-northeast direction (Shanxi → Xingtai → Hengshui → Cangzhou → Bohai Bay).

Estimating atmospheric nitrogen deposition within a large river basin using moss nitrogen and isotope signatures

Assessing flux and primary sources of the atmospheric nitrogen (N) deposition with high spatial resolution remained challenging. The epilithic moss is considered a suitable biological monitor to explore N deposition. Our study presented a detailed analysis of flux and major source contributions of ammonium (NH4+) and nitrate (NO3−) deposition using N and δ15N signatures of epilithic moss collected densely from the Yangtze River basin. The results showed a more negative δ15N and higher N concentration of the moss in cropland and urban area than in forest and grassland of the basin. A gradient of the estimated N deposition (9.6–34.0 kg ha−1 yr−1) occurred from the Tibetan Plateau to lower reaches, with amount of NH4+ was approximately three times higher than NO3− deposition. The contribution from volatilization to NH4+ deposition (33.28 ± 8.10%) was less than the contribution from combustion (66.72 ± 8.10%), inconsistent with the traditional findings that N fertilizer and livestock waste are the principal sources of NH3 emissions. Fossil fuel was the dominant sources of NO3− deposition, accounted for 70.22 ± 18.67%. From 2006 to 2019, the source contribution of N deposition in forest remained unchanged, while NH3 volatilization and fossil fuel emitted NOx in urban areas have increased. Our findings highlighted the importance of combustion sources to N deposition in the Yangtze River basin.

Application of a PM2.5 dispersion model in the Bangkok central business district for air quality management

Model simulations are conducted for fine particles diameter less than 2.5 microns (PM2.5) in the Chulalongkorn University area in the central business district of Bangkok, Thailand, where PM2.5 originating from road traffic is a recurring problem. For input to the American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model (AERMOD), an hourly continuous vehicle type classified emissions inventory is developed based on local traffic observations and published emissions factors. The simulation accounts for advected-in PM2.5 by hourly measurements from upwind stations. The result reveals a hotspot location near a nearby expressway with PM2.5 concentration peaking at 1-h and 24-h averages of 344 and 130 μg m−3, respectively. Source contribution analysis of the annual average PM2.5 at this hotspot suggests that the expressway contributes approximately 32% of the total PM2.5. Meanwhile, at receptor points farther from the expressway, ground-level urban roads contribute only 17.5% roadside PM2.5 concentrations, the remainder coming from outside the modeled area. A different source contribution breakdown by vehicle type suggests that heavy-duty vehicles contribute up to 21% of annual average PM2.5 at a location near the expressway. At a roadside receptor point farther from the expressway, the top contributors are light-duty (9%) and heavy-duty vehicles (6%). Advected-in PM2.5 dominates the overall PM2.5 concentrations, accounting for 64%–99% depending on the receptor point. The model performance for 24-h average prediction is acceptable. A scenario study is also performed to compare the potential effectiveness of two PM2.5 abatement measures.

Source contribution analysis of vehicle pass-by noise using a moving multi-band model based OPAX method

The conventional operational path analysis with eXogeneous inputs (OPAX) method has been widely used to investigate the vibration transfer paths. However, the single degree of freedom model is not available for the load identification of acoustic sources due to their unknown source behavior. And the alternative multi-band (MB) model can only offer a deterministic estimation to the load parameters. To assess the acoustic source contribution of vehicle pass-by noise (PBN), this paper develops the OPAX method by introducing a moving multi-band model (MMB). In this method, the vehicle acoustic sources are simplified as a combination of a series of point sources. The source loads are firstly parametrically modelled by MB models. Then the load parameters are solved several times in the MMB and estimated by the index of coefficient of variation, which suppresses the effect of nonlinear random noise and improves the load identification accuracy of acoustic sources. Finally, the source contributions to PBN at receivers can be derived. Both numerical studies and experimental validations are implemented to examine the performance of the proposed method. Results show that compared to the conventional OPAX method, the proposed method can provide a higher accuracy for the source contribution analysis of PBN.

Analysis of water source contributions and their impacts on hydrological structural connectivity in plain urban river network areas based on stable isotopes (δ2H, δ18O)

AbstractWater sources carry chemicals that can have a significant impact on the water environment of a river network, and understanding the contribution of different water sources to the river network can help to manage the pollution of the river network at its source. The hydrological connectivity of a river network affects the self‐purification capacity and flood prevention capacity of the river. Therefore, an isotope tracer approach was applied to determine the contribution rate of different water bodies to a river network and hydrological connectivity was quantified by introducing the retention rate. Changzhou city was selected as the study area because it is an urbanized city with the characteristics of a plain river network and it is faced with poor hydrological connectivity due to artificial constructions (dams and pumps) and human activity (urbanization). River water, well water (shallow groundwater), lake water and rainfall were collected during the flood season and nonflood season, and hydrogen and oxygen isotopes were determined. The temporal and spatial variations in hydrogen and oxygen isotopes in different water bodies and the state of the water cycle in different water bodies were analysed. IsoSource and MixSIAR models were established to analyse the contribution rate of river network water sources in the study area, and their effectiveness was compared. The results of the MixSIAR model were selected to evaluate the hydrological connectivity of the river network in the study area, providing a method to quantify the hydrological connectivity of specific rivers of the river network in Changzhou city. This method could also be applied to other urban plain river network areas to study river connectivity.

A pre-assessment and pollution prevention tool for indoor volatile organic compound simulations during the interior design stage

Volatile organic compounds (VOCs) are common indoor pollutants that can deteriorate indoor air quality. The pre-assessment of VOC concentrations aimed at pollution prevention during the interior design stage is effective in terms of time and expense for VOC control, as required by engineering applications. In this study, we introduce the concept, framework, and mathematical models of a pre-assessment and pollution prevention tool for VOC simulations. This tool considers multiple emissions sources and sinks, the interior construction schedule for the building materials, internal and external airflows, and air cleaning. The emissions/adsorption patterns from building materials were numerically coupled with the combined effects of the thermal environment, ventilation, indoor VOC concentrations, and emissions/adsorption from other materials at each time step, thereby improving the accuracy of the tool in engineering applications. The proposed tool was applied in an office building with multiple rooms and building materials to illustrate the simulation procedures and validate its effectiveness at VOC preventive control. The main emissions sources were determined and the selection of building materials was modified based on the simulation results. Field measurements were performed and the measured data were compared with the simulation results after construction completion. Differences between the simulated and measured results were predominantly within 0.02 mg/m3. The normalized mean square error was 0.11 and the mean relative standard deviation was 16.9%. This method can be used for indoor VOC concentration estimations, source contribution analysis, interior design scheme optimization, and ventilation rate requirement estimation, among other applications.

Identification of steering wheel vibration source of internal combustion forklifts based on wavelet coherence analysis

The vibration of the internal combustion balance forklift and other types of construction machinery, especially the vibration at the steering wheel, has a significant impact on drivers’ work efficiency and comfort. Since most of the vibration sources of internal combustion forklifts during the working condition are nonstationary, how to accurately identify the nonstationary vibration sources that cause steering wheel vibration is of importance and challenging. This paper proposed an identification method for steering wheel vibration sources based on wavelet coherence analysis. Wavelet packet energy decomposition was used to determine the distribution of vibration energy at the steering wheel of an inter-combustion engine forklift truck for both idling and wide open throttle (WOT) conditions. Coherence analysis and source contribution analysis for the steering wheel vibration were carried out using FFT (coherence) and CWT (wavelet coherence) methods. The two methods and the results from the two conditions were compared and discussed. Results showed that wavelet coherence analysis can better identify the signal feature at low frequencies and provide global contribution analysis for the whole nonstationary conditions. In addition, it was found that the main vibration sources of the steering wheel vibration under the idling and WOT condition were different. A simple confirmation test was further conducted which proved the effectiveness of the proposed method.

Response surface model based emission source contribution and meteorological pattern analysis in ozone polluted days

Urban and regional ozone (O3) pollution is a public health concern and causes damage to ecosystems. Due to the diverse emission sources of O3 precursors and the complex interactions of air dispersion and chemistry, identifying the contributing sources of O3 pollution requires integrated analysis to guide emission reduction plans. In this study, the meteorological characteristics leading to O3 polluted days (in which the maximum daily 8–h average O3 concentration is higher than the China Class II National O3 Standard (160 μg/m3)) in Guangzhou (GZ, China) were analyzed based on data from 2019. The O3 formation regimes and source apportionments under various prevailing wind directions were evaluated using a Response Surface Modeling (RSM) approach. The results showed that O3 polluted days in 2019 could be classified into four types of synoptic patterns (i.e., cyclone, anticyclone, trough, and high pressure approaching to sea) and were strongly correlated with high ambient temperature, low relative humidity, low wind speed, variable prevailing wind directions. Additionally, the cyclone pattern strongly promoted O3 formation due to its peripheral subsidence. The O3 formation was nitrogen oxides (NOx)-limited under the northerly wind, while volatile organic compounds (VOC)-limited under other prevailing wind directions. Anthropogenic emissions contributed largely to the O3 formation (54–78%) under the westerly, southwesterly, easterly, southeasterly, or southerly wind, but only moderately (35–47%) under the northerly or northeasterly wind. Furthermore, as for anthropogenic contributions, local emission contributions were the largest (39–60%) regardless of prevailing wind directions, especially the local NOx contributions (19–43%); the dominant upwind regional emissions contributed 12–46% (e.g., contributions from Dongguan were 12–20% under the southeasterly wind). The emission control strategies for O3 polluted days should focus on local emission sources in conjunction with the emission reduction of upwind regional sources.

Assessment of regional carbon monoxide simulations over Africa and insights into source attribution and regional transport

This study assesses the quality of carbon monoxide (CO) simulations from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and analyzes different processes controlling CO distribution in Africa. Sixteen CO tracers are implemented in WRF-Chem to track CO from different sources and regions. The model captures the seasonal cycles of surface temperature fairly well (r > 0.75 at most sites) and reproduces key features and variability of precipitation, wind speed, and planetary boundary layer height with errors typically seen in application of a regional model over such a large domain. The model also captures CO concentration hotspots and vertical gradients but underestimates the Measurements of Pollution in the Troposphere (MOPITT) retrieved total column with a larger underestimation in northern hemispheric Africa. The model also underestimates MOPITT and aircraft observed CO profiles throughout the troposphere. We discuss outdated anthropogenic emissions, low biomass burning emissions, and well-known low biases in global models of CO as potential contributors to the discrepancies between the regional model and MOPITT CO. Source contribution analysis of surface CO showed that all sources contribute to CO in Africa with anthropogenic emissions dominating in the urban regions, fire emissions dominating in central Africa, CO inflow from domain boundaries dominating in North Africa and the oceanic regions, and photochemical production dominance aligning with seasonal changes in sunlight. We also show that regional transport of CO emissions between African regions also play a very important role in controlling surface CO distribution in Africa. In some regions, more than 50% of the anthropogenic or biomass burning emitted CO comes from sources located outside those regions, highlighting the need for regional coordination and cooperation for air quality management in Africa.

Spatiotemporal variations of carbon dioxide (CO2) at Urban neighborhood scale: Characterization of distribution patterns and contributions of emission sources

• Spatiotemporal distributions were analyzed for urban CO 2 concentration via field study. • CO 2 in urban neighborhoods exhibited notable daily and seasonal variations. • The CO 2 showed a gradual decrease in roadside, residential, and green space areas. • Random Forest and eXtreme Gradient Boosting were used for source contribution analysis. • Traffic source had the largest contribution to the CO 2 followed by green space. The gradual increase in atmospheric carbon dioxide (CO 2 ) concentrations has attracted worldwide attention for its strong relationships with global climate change. Considerable efforts are being undertaken to characterize spatiotemporal variations of CO 2 at a city, regional and national level, aiming at providing pipelines for carbon emission reduction. However, there is scarce knowledge of how CO 2 at the urban neighborhood scale is produced and distributed in the context of time and space, which is useful to accurately target source contributions from the ground up and reduce carbon emissions at a fine-grained scale. In this study, mobile measurements of CO 2 concentrations were made in a 2 km × 2 km urban area covering different land use types to separately characterize the spatiotemporal distribution patterns of CO 2 in roadside, residential and green space areas. The results show that CO 2 concentrations in the late afternoon (Local time, UTC+8, LT 17–18) were higher than those at noon (LT 11–12), and that CO 2 concentrations in winter were higher than those in summer. The roadside areas exhibited the highest CO 2 concentration level of 452.66 ± 20.59 ppm, followed by residential areas (436.34 ± 27.02 ppm) and green space areas (428.98 ± 20.49 ppm). The result indicates that traffic sources brought more carbon emissions and contributed to a significant increase in CO 2 concentrations, while urban greenery caused more carbon absorptions and reduced CO 2 concentrations. This can be further confirmed by the observations that CO 2 concentrations in the roadside neighborhood showed a strong positive correlation (R 2 = 0.86) with ambient traffic flow. Then two machine learning models, i.e., Random Forest and eXtreme Gradient Boost, were developed to quantify the individual contribution from different carbon emission sources to the CO 2 distributions, including traffic flow, greening rate, and domestic energy consumption. The results show that traffic-related carbon emissions were the most important influencing factor and accounted for approximately 60% of ambient CO 2 concentrations, followed by greening rate (20%) and domestic energy consumption (10%). These findings can provide insights into spatiotemporal distributions and source contributions of CO 2 in urban neighborhoods and show huge potentials for reducing urban carbon emissions at a fine-grained scale.

What Are the Sectors Contributing to the Exceedance of European Air Quality Standards over the Iberian Peninsula? A Source Contribution Analysis

The Iberian Peninsula, located in southwestern Europe, is exposed to frequent exceedances of different threshold and limit values of air pollution, mainly related to particulate matter, ozone, and nitrous oxide. Source apportionment modeling represents a useful modeling tool for evaluating the contribution of different emission sources or sectors and for designing useful mitigation strategies. In this sense, this work assesses the impact of various emission sectors on air pollution levels over the Iberian Peninsula using a source contribution analysis (zero-out method). The methodology includes the use of the regional WRF + CHIMERE modeling system (coupled to EMEP emissions). In order to represent the sensitivity of the chemistry and transport of gas-phase pollutants and aerosols, several emission sectors have been zeroed-out to quantify the influence of different sources in the area, such as on-road traffic or other mobile sources, combustion in energy generation, industrial emissions or agriculture, among others. The sensitivity analysis indicates that large reductions of precursor emissions (coming mainly from energy generation, road traffic, and maritime-harbor emissions) are needed for improving air quality and attaining the thresholds set in the European Directive 2008/50/EC over the Iberian Peninsula.

Response Surface Model Based Emission Source Contribution and Meteorological Pattern Analysis in Ozone Polluted Days

Response Surface Model Based Emission Source Contribution and Meteorological Pattern Analysis in Ozone Polluted Days

Source contribution analysis of PM2.5 using Response Surface Model and Particulate Source Apportionment Technology over the PRD region, China

Identifying the emission source contributions to PM2.5 is essential for a sound PM2.5 pollution control policy. In this study, we conduct a comparative analysis of PM2.5 source contributions over the Pearl River Delta (PRD) region of China using two advanced source contribution modeling techniques: Response Surface Model (RSM) and Particulate Source Apportionment Technology (PSAT). Our comparative analyses show that RSM and PSAT can both reasonably predict the contribution of primary PM2.5 emission sources to PM2.5 formation due to its linear nature. For the secondary PM2.5 formed by the nonlinear reactions among PM2.5 precursors, however, our study shows that PSAT appears to have limitations in quantifying the nonlinear contribution of PM2.5 precursors to emission reductions, while RSM seems to better address the nonlinear relationship among PM2.5 precursors (e.g., PM2.5 disbenefits due to local NOx emission reductions in major cities with high NOx emissions). The pilot study case results show that for the ambient PM2.5 in the central cities (Guangzhou, Shenzhen, Foshan, Dongguan, and Zhongshan) of the PRD, the regional source emissions contribute the most by 42–66%; the dust emissions are the top contribution sources (29–34% by RSM and 27–31% by PSAT), and the mobile sources are listed as the secondary contributors accounting for 16–25% by RSM and 19–30% by PSAT among the anthropogenic emission sources. The city-scale cooperation on emission reductions and the enhancement of dust and mobile emission control are recommended to effectively reduce the ambient PM2.5 concentration in the PRD.

Contribution of anthropogenic and natural sources in PM10 during North African dust events in Southern Europe

The influence of North African (NAF) dust events on the air quality at the regional level (12 representative monitoring stations) in Southern Europe during a long time series (2007–2014) was studied. PM10 levels and chemical composition were separated by Atlantic (ATL) and NAF air masses. An increase in the average PM10 concentrations was observed on sampling days with NAF dust influence (42 μg m−3) when compared to ATL air masses (29 μg m−3). Major compounds such as crustal components and secondary inorganic compounds (SIC), as well as toxic trace elements derived from industrial emissions, also showed higher concentrations of NAF events. A source contribution analysis using positive matrix factorisation (PMF) 5.0 of the PM10 chemical data, discriminating ATL and NAF air mass origins, allowed the identification of five sources: crustal, sea salt, traffic, regional, and industrial. A higher contribution (74%) of the natural sources to PM10 concentrations was confirmed under NAF episodes compared with ATL. Furthermore, there was an increase in anthropogenic sources during these events (51%), indicating the important influence of the NAF air masses on these sources. The results of this study highlight that environmental managers should take appropriate actions to reduce local emissions during NAF events to ensure good air quality.

Particulate pollution over an urban Himalayan site: Temporal variability, impact of meteorology and potential source regions

Five-year (2013–2017) particulate matter (PM) data observed at an urban site, Srinagar, Kashmir Himalaya, India was used to examine the temporal variability, meteorological impacts and potential source regions of PM. The daily mean PM10 and PM2.5 concentration was 135 ± 112 μg/m3 and 87 ± 93 μg/m3 respectively with significant intra- and inter-daily variation. The annual PM10 and PM2.5 concentration was 2.0–3.2 and 1.7–2.8 times higher than the annual Indian National Ambient Air Quality Standards (PM10 = 60 μg/m3 and PM2.5 = 40 μg/m3). PM concentration shows a bimodal diurnal pattern with morning and evening peaks, which coincide with the increased anthropogenic activity and shallow planetary boundary layer (PBL). The combined effect of the low temperature, low wind speed, shallow and stable PBL and geomorphic setup of Kashmir valley leads to the accumulation of particulate pollution during autumn and winter and the converse meteorological conditions leads to dispersion, dilution and deposition during spring and summer. High precipitation rate (>15 mm/day) removes the coarse particles (PM10) more efficiently than fine particles (PM2.5), while as the moderate to high humid conditions (55–95%) leads to the accumulation and growth of more PM. It was observed that ~80% of the air masses arriving at the site during spring, autumn and winter are westerlies. Source contribution analysis revealed that highly potential source regions of PM at the site are neighboring Pakistan, Afghanistan, parts of Iran and Trans-Gangetic Plains, which could contribute high concentration of the PM10 (>250 μg/m3) and PM2.5 (>150 μg/m3) during autumn and winter. The high PM load observed at the site during autumn and winter, with major contribution from the anthropogenic source emissions like biomass and coal burning, fossil fuel combustion and suspension of road dust, is aggravated by the geomorphic and meteorological setup of the Kashmir valley.

Source impact and contribution analysis of ambient ozone using multi-modeling approaches over the Pearl River Delta region, China

Quantification of source impacts and contributions is a key element for the design of effective air pollution control policies. In this study, O3 source impacts and contributions were comprehensively assessed over the Pearl River Delta (PRD) region of China using brute-force method (BFM), response surface modeling with BFM (RSM-BFM) and differential method (RSM-DM) respectively, high-order decoupled direct method (HDDM), and ozone source apportionment technology (OSAT). The multi-modeling comparison results indicated that under typical nonlinear atmospheric conditions during the O3 formation, BFM, RSM-BFM, and HDDM seemed to be appropriate for assessing the impact of single source emissions; however, the results of HDDM could deviate from those of BFM when the emission reduction ratio was higher than 50 %. Under multi-source control scenarios, the results of source contribution analyses obtained from RSM-DM and OSAT were reasonably well, but the performance of OSAT was limited by its capability in representing the nonlinearity of O3 response to emission reductions of its precursors, particularly NOx. The results of this pilot study in the PRD showed that the RSM-DM appeared to replicate the nonlinearity of O3 chemistry reasonably well (e.g., O3 disbenefits due to local NOx emission reductions in Guangzhou city). Based on the source contribution results, on-road mobile (including both NOx and VOC emissions) and industrial process (mainly VOC emissions) sources were identified as two major contribution sectors by both RSM-DM and OSAT, contributing an average of 31.5 % and 11.4 % (estimated by RSM-DM) and 29.2 % and 13.0 % (estimated by OSAT) respectively to O3 formation in 9 cities of the PRD. Therefore, the reinforced emission reductions on NOx and VOC from on-road mobile and industrial process sources in the central cities (i.e., Guangzhou, Foshan, Dongguan, Shenzhen, and Zhongshan) were suggested to effectively mitigate the ambient O3 levels in the PRD.

Prediction and Source Contribution Analysis of PM2.5 Using a Combined FLEXPART Model and Bayesian Method over the Beijing-Tianjin-Hebei Region in China

Fine particulate matter (PM2.5) has a serious impact on human health. Forecasting PM2.5 levels and analyzing the pollution sources of PM2.5 are of great significance. In this study, the Lagrangian particle dispersion (LPD) model was developed by combining the FLEXPART model and the Bayesian inventory optimization method. The LPD model has the capacity for real-time forecasting and determination of pollution sources of PM2.5, which refers to the contribution ratio and spatial distribution of each type of pollution (industry, power, residential, and transportation). In this study, we applied the LPD model to the Beijing-Tianjin-Hebei (BTH) region to optimize the a priori PM2.5 emission inventory estimates during 15–20 March 2018. The results show that (1) the a priori estimates have a certain degree of overestimation compared with the a posteriori flux of PM2.5 for most areas of BTH; (2) after optimization, the correlation coefficient (R) between the forecasted and observed PM2.5 concentration increased by an average of approximately 10%, the root mean square error (RMSE) decreased by 30%, and the IOA (index of agreement) index increased by 16% at four observation sites (Aotizhongxin_Beijing, Beichenkejiyuanqu_Tianjin, Dahuoquan_Xintai, and Renmingongyuan_Zhangjiakou); and (3) the main sources of pollution at the four sites mainly originated from industrial and residential emissions, while power factory and transportation pollution accounted for only a small proportion. The concentration of PM2.5 forecasts and pollution sources in each type of analysis can be used as corresponding reference information for environmental governance and protection of public health.

Source contribution analysis of nutrient pollution in a P-rich watershed: Implications for integrated water quality management.

It is still a great challenge to address nutrient pollution issues caused by various point sources and non-point sources on the watershed scale. Source contribution analysis based on watershed modeling can help watershed managers identify major pollution sources, propose effective management plans and make smart decisions. This study demonstrated a technical procedure for addressing watershed-scale water pollution problems in an agriculture-dominated watershed, using the Dengsha River Watershed (DRW) in Dalian, China as an example. The SWAT model was improved by considering the constraints of soil nutrient concentration, i.e., nitrogen (N) and phosphorus (P), when modeling the nutrient uptake by a typical crop, corn. Then the modified SWAT model was used to quantify the contributions of all known pollution sources to the N and P pollution in the DRW. The results showed that crop production and trans-administrative wastewater discharge were the two dominant sources of nutrient pollution. This study further examined the responses of nutrient loss and crop yield to different fertilizer application schemes. The results showed that N fertilizer was the limiting factor for crop yield and that excessive levels of P were stored in the agricultural soils of the DRW. An N fertilizer application rate of approximately 40% of the current rate was suggested to balance water quality and environmental protection with crop production. The long-term impact of legacy P was investigated with a 100-year future simulation that showed the crop growth could maintain for 12 years even after P fertilization ceased. Our study highlights the need to consider source attribution, fertilizer application and legacy P impacts in agriculture-dominated watersheds. The analysis framework used in this study can provide a scientifically sound procedure for formulating adaptive and sustainable nutrient management strategies in other study areas.

Effects of Fine Grid NO2 Modeling around a Thermal Power Station: A Case Study

Objective: The objective of this study was to assess the outcome of fine grid modeling on top of a courser grid to predict the NO2 concentration more accurately in an airshed and at sensitive receptors. Methods: This study assessed the cumulative NO2 impact of all major emission sources in an airshed using USEPA regulatory model AERMOD. A 50 km by 50 km airshed is considered in the study. Micro-environmental pollution was modeled using a refined grid analysis. An area (4 km x 4 km) close to GTPS, with receptors at every 150 m apart and a further fine grid (500 m x 500 m) with receptors at every 50 m apart, was modeled. Results: Coarse grid modeling showed annual average NO2 concentration levels within applicable standards. However, when fine grid modeling was conducted, the predicted annual average NO2 concentration levels were found to exceed World Bank Guidelines and Bangladesh Standards. A source contribution analysis showed that a quick rental power plant (natural gas generators) without proper stacks contributed a significant portion of the maximum 1-hr and annual average NO2 concentration (76% and 86%, respectively). Conclusion: The findings of fine grid modeling can be used at the policy level of the government to enforce environmental regulations on the minimum height requirements of stacks and city planning, avoiding downwind directions and the close proximity of powerplants to safeguard human health.