Pollution Source Apportionment Research Articles

An Ozone Episode in the Urban Agglomerations along the Yangtze River in Jiangsu Province: Pollution Characteristics and Source Apportionment

A severe ozone episode occurred in cities along the Yangtze River of Jiangsu Province (UAYRJS) from 6 to 8 September 2022, with daily maximum 8-h average ozone concentrations in the range of 65.8–119 ppb, peaking in Nanjing on 7 September. We used the air quality model WRF-CMAQ-ISAM and the Lagrange trajectory model HYSPLIT to quantify the ozone contribution of each region and analyze the causes and regional transmission pathways of ozone pollution in the UAYRJS. Based on simulated emissions, we also estimated the contribution of biogenic volatile organic compounds. We found that weather has a negative impact on pollution, and ozone pollution tracks the movement of the Western Pacific Subtropical High. UAYRJS was affected by oceanic pollution, and there was a mutual influence among the area’s cities. On 6 September, the ozone in UAYRJS was mostly locally generated (50–98%); on 7 September, it was dominated by extra-regional transport (50–80%). Isoprene concentrations in UAYRJS increased by 0.03–0.1 ppb on 6 and 7 September compared with 5 September. Sensitivity testing showed that the hourly ozone concentration increased by 0.1–27.8 ppb (7.6–19.1%) under the influence of biogenic emissions. The results provide a scientific basis for future ozone control measures.

Arsenic-contaminated groundwater of the Western Banat (Pannonian basin): Hydrogeochemical appraisal, pollution source apportionment, and Monte Carlo simulation of source-specific health risks.

Due to rapid urbanization and industrial growth, groundwater globally is continuously deteriorating, posing significant health risks to humans. This study employed a comprehensive methodology to analyze groundwater in the Western Banat Plain (Serbia). Using Piper and Gibbs plots, hydrogeochemistry was assessed, while the entropy-weighted water quality index (EWQI) was used to evaluate groundwater quality. Pollution sources were identified using positive matrix factorization (PMF) accompanied by Pearson correlation and hierarchical cluster analysis, while Monte Carlo simulation assessed health risks associated with groundwater consumption. Results showed that groundwater, mainly Ca-Mg-HCO3 type, is mostly suitable for drinking. Geogenic pollution, agricultural activities, and sewage were major pollution sources. Consumption of contaminated groundwater poses serious non-carcinogenic and carcinogenic health risks. Additionally, arsenic from geogenic source was found to be the main health risks contributor, considering its worryingly elevated concentration, ranging up to 364 μg/L. These findings will be valuable for decision-makers and researchers in managing groundwater vulnerability. PRACTITIONER POINTS: Groundwater is severely contaminated with As in the northern part of the study area. The predominant hydrochemical type of groundwater in the area is Ca-Mg-HCO3. The PMF method apportioned three groundwater pollution sources. Monte Carlo identified rock dissolution as the primary health risk contributor. Health risks and mortality in the study area are positively correlated.

Pollution characteristics, source apportionment, and health risk assessment of PM10 and PM2.5 in rooftop and kerbside environment of Lanzhou, NW China.

To investigate air pollution in the kerbside environment and its associated human health risks, a study was conducted in Lanzhou during December 2018, as well as in April, June, and September 2019. The research aimed to characterize the composition of PM10 and PM2.5, including elements, ions, and carbonaceous components, at both rooftop and kerbside locations. Additionally, source apportionment and health risk assessment were conducted. The results showed that the average mass concentrations of PM10 on the rooftop were 176.01 ± 83.23μg/m3, and for PM2.5, it was 94.07 ± 64.89μg/m3. The PM10 and PM2.5 levels at the kerbside are 2.21 times and 1.79 times, respectively, greater than those on the rooftop. Moreover, the concentrations of elements, ions, and carbonaceous components in kerbside PM were higher than those at the rooftop location. Chemical mass closure analysis identified various sources, including organic matter, mineral dust, secondary ions, other ions, elements, and other components. In comparison to rooftop particulate matter (PM), mineral dust makes a more substantial contribution to kerbside PM. Secondary ions show an opposite trend, making a greater contribution to rooftop PM. The contribution of organic components within PM of the same particle size remains relatively consistent. The outcome of the health risk assessment indicates that Co, Cd, and As in PM within the kerbside and rooftop environments do not pose a notable carcinogenic risk. However, Al and Mn do present specific non-carcinogenic risks, particularly in the kerbside environment. Furthermore, children experience elevated non-carcinogenic risk compared to adults. These findings can serve as a scientific foundation for formulating policies within the local health department.

Quantitative tracing the sources and human risk assessment of complex soil pollution in an industrial park

Pollution in industrial parks has long been characterized by complex pollution sources and difficulties in identifying pollutant origins. This study focuses on a typical industrial park consisting of 11 factories (F1–F11) including organic pigment, inorganic pigment, and chemical factories in Hunan Province, China, here, a total of 327 sample points were surveyed. Eight pollutants (Mn, Cd, As, Co, NH3–N, l, 1,2-Trichloroethane, chlorobenzene, and petroleum hydrocarbons) were classified as contaminants of concern (COCs). This study assessed the contributions of driving factors to the distribution of COCs in the soil. Pollutant source apportionment was conducted using positive matrix factorization (PMF) and random forest (RF). The results revealed that the main factors driving pollution are groundwater migration, non-compliant emissions, leaks during production, and interactions among pollutants. The primary pollution sources were four chemical factories and an inorganic pigment factory. Source 5 demonstrates significant correlations with TCA (29.6%), CB (30%), and As (31.6%). Two chemical factories (F7 and F10) are the most significant pollution source with a risk assessment contribution rate of more than 60%. The present study sheds some light on the contamination characteristics, source apportionment and source-health risk assessment of COCs in industrial park. By utilizing the proposed research framework, decision-makers can effectively prioritize and address identified pollution sources.

Pollution characteristics, source apportionment and absorption spectra of size-resolved PAHs in atmospheric particles in a cold megacity of China

Polycyclic aromatic hydrocarbons (PAHs) have the capability for solar radiation absorption related to climate forcing. Herein, pollution characteristics and absorption spectra of size-resolved PAHs in atmospheric particles in a cold megacity were comprehensively investigated. The mean concentrations of Σ18PAHs in all the 11 particle size ranges were 3.95 ± 4.77 × 104 pg/m3 and 2.17 ± 1.54 × 103 pg/m3 in heating period (HP) and non-heating period (NHP), respectively. Except for most PAHs with 2 and 3 benzene rings in NHP, most other PAHs showed a unimodal distribution pattern with the peak at 0.56–1.0 µm in both periods, which was caused by PAH emission sources. The PAH-related climate forcing was mainly caused by the solar radiation absorptions at ∼325 (∼330) nm and ∼365 nm. In general, the absorption intensities were higher in HP than NHP. The absorption intensity in the particle size range of 0.56–1.0 µm was the highest, and benzo[e]pyrene was the dominant contributor. In colder periods in HP, higher PAH concentrations caused more intensive PAH-related climate forcing. This study provided new insights for pollution characteristics and absorption spectra of size-resolved PAHs in atmospheric particles, which will be useful for better understanding PAH-related climate forcing.

Novel approach for identifying VOC emission characteristics based on mobile monitoring platform data and deep learning: Application of source apportionment in a chemical industrial park

Refined volatile organic compound (VOC) emission characteristics are crucial for accurate source apportionment in chemical industrial parks. The data from mobile monitoring platforms in chemical industrial parks contain pollution information that is not intuitively displayed, requiring further excavation. A novel approach was proposed to identify VOC emission characteristics using the class activation map (CAM) technology of convolutional neural network (CNN), which was applied on the mobile monitoring platform data (MD) derived from a typical fine chemical industrial park. It converts a large amount of monitoring data with high spatiotemporal complexity into simple and interpretable characteristic maps, effectively improving the identification effect of VOC emission characteristics, supporting more accurate source apportionment of VOC pollution around the park. Using this method, the VOC emission characteristics of eight key factories were identified. VOC source apportionment in the park was conducted for one day using a positive matrix factorization (PMF) model and seven combined factor profiles (CFPs) were calculated. Based on the identified VOC emission characteristics, the main pollution sources and their contributions to surrounding schools and residential areas were determined, revealing that one pesticide factory (named LKA) had the highest contribution ratio. The source apportionment results indicated that the impact of the chemical industrial park on the surrounding areas varied from morning to afternoon, which to some extent reflected the intermittent production methods employed for fine chemicals.

Hydro-meteorological factors and inflowing nutrients drive water quality in an impounded lake of China's South-to-North Water Diversion Project.

Freshwater lakes play a vital role in global hydrological and biogeochemical cycles, serving various functions and maintaining ecological balance. However, freshwater resources are more vulnerable to deterioration due to multiple stressors. Gaoyou Lake is one of the impounded lakes of the Eastern route of South-to-North Water Diversion Project in China, and as an important source of drinking water, the lake has been routinely monitored. Long-term monitoring of water quality in Gaoyou Lake showed that concentrations of nutrients and chlorophyll a as well as trophic state in the water column increased while water transparency decreased, indicating that the water quality has declined during the last 12years. Specifically, there was a notable and statistically significant increase in chlorophyll a concentrations, averaging an annual rate of 9.9%. Despite a slight decline in trophic level index until 2014, subsequent years saw an upward trend, ranging from 50.7 to 56.4 and indicating a light eutrophic state. Spatially, the western area displayed higher nutrient and chlorophyll a concentrations. Changes in hydro-meteorological variables and nutrients from inflowing rivers were the main factors correlated with water quality in Gaoyou Lake. Thus, pollution source apportionment and management within Huaihe River basin should be considered to reduce the external loadings of nutrients in order to improve and sustain long-term water quality.

Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model

Effective evaluation of water quality and accurate quantification of pollution sources are essential for the sustainable use of water resources. Although water quality index (WQI) and positive matrix factorization (PMF) models have been proven to be applicable for surface water quality assessments and pollution source apportionments, these models still have potential for further development in today's data-driven, rapidly evolving technological era. This study coupled a machine learning technique, the random forest model, with WQI and PMF models to enhance their ability to analyze water pollution issues. Monitoring data of 12 water quality indicators from six sites along the Minjiang River from 2015 to 2020 were used to build a WQI model for determining the spatiotemporal water quality characteristics. Then, coupled with the random forest model, the importance of 12 indicators relative to the WQI was assessed. The total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (CODCr), dissolved oxygen (DO), and five-day biochemical oxygen demand (BOD5) were identified as the top five significant parameters influencing water quality in the region. The improved WQI model constructed based on key parameters enabled high-precision (R2 = 0.9696) water quality prediction. Furthermore, the feature importance of the indicators was used as weights to adjust the results of the PMF model, allowing for a more reasonable pollutant source apportionment and revealing potential driving factors of variations in water quality. The final contributions of pollution sources in descending order were agricultural activities (30.26%), domestic sewage (29.07%), industrial wastewater (26.25%), seasonal factors (6.45%), soil erosion (6.19%), and unidentified sources (1.78%). This study provides a new perspective for a comprehensive understanding of the water pollution characteristics of rivers, and offers valuable references for the development of targeted strategies for water quality improvement.

An Exploratory Data Analysis (EDA) Tool for Tracer Selection in Sediment and Pollution Fingerprinting Studies

Tracer selection is a critical step for any mixing model that uses the information of multiple variables to find the most likely solution for sediment or pollution source apportionment. The purpose of this work was to design an Exploratory Data Analysis (EDA) tool for qualitative and quantitative interpretation of tracer selection based on boxplots. This tool establishes a set of conditions presented as a decision tree to support the user in tracer selection decisions where current systems are either too statistically driven, which limits user interaction with the data, or rely too heavily on judgement and prior knowledge of tracer behaviour. This method extends upon the classical “range test” by incorporating a set of conditions and thresholds, creating a semi-quantitative method with a visual component to improve the tracer selection process and enable targeted consideration of geochemical behaviours for conservativism purposes. We present the results from experimental mixtures using the mixing model MixSIAR to demonstrate the method and illustrate the value of this approach for improved tracer selection. The tool, which is written in R, is freely available for use across a wide range of source apportionment applications.

Characteristics and Identification Priority Source of Heavy Metals Pollution in Farmland Soils in the Yellow River Basin

Assessments of the soil environmental quality of farmland and pollution source apportionment are the foundation for ensuring national food security and agricultural sustainable development, as well as an important prerequisite for the pursuit to keep our lands clean. This study evaluated the characteristics of heavy metal pollution in farmland soils in the Yellow River Basin from 2000 to 2023, based on the data of heavy metal contents including As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, using the geo-accumulation index method. Source apportionment was conducted by employing a positive matrix factorization (PMF) model. The probabilistic health risks were evaluated by coupling Monte Carlo simulation with a human health risk assessment model, and priority pollution sources and elements were identified. The results showed that:① the average content of all heavy metals in farmland soils within the study area was lower than the screening values specified in the soil environment quality risk control standard for soil contamination of agriculture land (GB 15618-2018) (pH>7.5). However, the contents of Cd, As, and Zn in the samples exceeded their screening values, with percentages of 21.69%, 5.56%, and 1.23%, respectively, with Cd having the highest rate of exceedance. ② Hg and Cd were moderately polluted, Cu and Pb were slightly polluted, and the other elements were not polluted. ③ The main sources of heavy metals in farmland soil were traffic-industrial sources, natural-agricultural sources, industrial-natural sources, and agricultural-industrial sources, with contribution rates of 37.04%, 26.69%, 21.72%, and 14.55%, respectively. ④ Heavy metals in farmland soil posed carcinogenic health risks to adults and children but did not have non-carcinogenic risks; As and Cd were priority control elements for human health risks, and industrial-natural sources and agricultural-industrial sources were priority control sources in the study area.

Pollution Characteristics, Source Apportionment, and Meteorological Response of Water-soluble Ions in PM2.5 in Xinxiang, North China

Pollution variation, source characteristics, and meteorological effects of water-soluble inorganic ions (WSIIs) in PM2.5 were analyzed in Xinxiang city, Henan Province. PM2.5 samples and their chemical components were monitored online by using URG-9000 in four seasons:winter (January, 2022), spring (April, 2022), summer (July, 2022), and fall (October, 2022). The results showed that the TWSIIs had the same seasonal fluctuations as PM2.5. The average seasonal concentrations of WSIIs ranged from 19.62-72.15 μg·m-3, accounting for more than 60% of PM2.5, demonstrating that WSIIs were the major components of PM2.5. The annual concentration value of NO3-/SO42- was 2.11, which showed an increasing trend, suggesting predominantly mobile sources for secondary inorganic aerosols (SNA). Further, the molar concentration value [NH4+]/[NO3-] was 1.95, demonstrating that agriculture emissions were the dominant contributors to atmospheric nitrogen. Furthermore, the backward trajectory analysis showed that the concentrations of Ca2+ and Mg2+ were higher when the northeasterly wind prevailed and the wind speed was high. High values of SOR and NOR were correlated with low temperatures and high relative humidity (T < 8℃, RH > 60%), demonstrating that more gaseous precursors were converted into sulfate and nitrate. At high temperatures (T > 24℃), there was no apparent high NOR value like that for SOR, mainly due to the decomposition of NH4NO3 at high temperatures. Finally, backward trajectories associated with the PMF-resolved results were used to explore the regional transport characteristics. The results illustrated that dust sources in the study areas were mainly influenced by air trajectories originating from the northwest regions, whereas secondary sulfate, secondary nitrate, and biomass sources contributed more to WSIIs when wind speed and altitude air masses were low in the area surrounding the observation site.

Pollution Characteristics and Source Apportionment of Black Carbon Aerosols during Spring in Beijing.

Black carbon (BC) aerosols are important for absorbing aerosols, affecting global climate change and regional air quality, and potentially harming human health. From March to May 2023, we investigated black carbon aerosol levels and air pollution in Beijing. Employing methods such as linear regression, Potential Source Contribution Function (PSCF) and Concentration-Weighted Trajectory (CWT), we analyzed the characteristics and sources of black carbon aerosols in the region. Results indicate that the light absorption coefficients of BC and BrC decrease with increasing wavelength, with BrC accounting for less than 40% at 370 nm. Daily variations in BC and PM2.5 concentrations exhibit similar trends, peaking in March, and BC displays a distinct bimodal hourly concentration structure during this period. Aethalometer model results suggest that liquid fuel combustion contributes significantly to black carbon (1.08 ± 0.71 μg·m-3), surpassing the contribution from solid fuel combustion (0.31 ± 0.2 μg·m-3). Furthermore, the significant positive correlation between BC and CO suggests that BC emissions in Beijing predominantly result from liquid fuel combustion. Potential source area analysis indicates that air masses of spring in Beijing mainly originate from the northwest (40.93%), while potential source areas for BC are predominantly distributed in the Beijing-Tianjin-Hebei region, as well as parts of the Shandong, Shanxi and Henan provinces. Moreover, this study reveals that dust processes during spring in Beijing have a limited impact on black carbon concentrations. This study's findings support controlling pollution in Beijing and improving regional air quality.

Source apportionment and risk assessment of soil heavy metals in the Huangshui River Basin using a hybrid model

Source apportionment and risk assessment of soil heavy metals (HMs) are essential for pollution control. However, inherent limitations in receptor models hinder accurate source apportionment, impacting outcomes of source-oriented risk assessment. A hybrid model was employed by combining two receptor models, absolute principal component score/multiple linear regression (APCS/MLR) and positive matrix factorization (PMF) models. Four primary pollution sources were identified, followed by an assessment of source-oriented environmental and human health risks in the Huangshui River Basin. Results revealed that Cr, Cd, and Ni average concentrations surpassed their background by 3.1, 2.1, and 1.9 times, respectively. Additionally, 54.29%, 13.34%, and 18.09% of Igeo values for Cr, Cd, and Ni were classified as “moderately contamination” or higher. Natural sources significantly influence Cu (90.2%) and As (71.4%). Cr (63.4%) and Ni (77.9%) mainly originated from agriculture and industry, respectively. Transportation sources emerged as the primary contributors to Pb (59.6%), Zn (48.6%), and Cd (47.2%). The environmental risk level in this study area remained acceptable. Ecological risk was mainly attributed to industrial activities (46.6%) and transportation (37.6%), with Cd being the predominant metal responsible for this risk. Although the noncarcinogenic risk was negligible for all populations, the carcinogenic risk demands attention, particularly concerning children. Industrial sources (67.0%) and Ni were identified as the main contributors to the carcinogenic risk. This study represents an attempt to develop a hybrid model, providing an effective approach to combine models for the accurate apportionment of pollution sources and the reduction of risks.

Source apportionment based on EEM-PARAFAC combined with microbial tracing model and its implication in complex pollution area, Wujin District, China

Further improving the quality of surface water is becoming more difficult after the control of main point-sources, especially in the complex pollution area with mixed industrial and agricultural productions, whereas the pollution source apportionment might be the key to quantify different pollution sources and developing some effective measures. In this study, a technical framework for source apportionment based on three-dimensional fluorescence and microbial traceability model is developed. Based on screening of the main environmental factors and their spatiotemporal characteristics, potential pollution sources have been tentatively identified. Then, the pollution sources are further tested based on the analysis of fluorescence excitation-emission matrix (EEM) and the similarity of fluorescence components in surface water and potential pollution sources. At the same time, the correlation between microbial species and pollution sources is constructed by analyzing the spatiotemporal characteristics of microbial composition and the response of main species to environmental factors. Therefore, pollution source apportionment is quantified using PCA-APCS-MLR, Fast Expectation-maximization for Microbial Source Tracking (FEAST), and Bayesian community-wide culture-independent microbial source tracking (SourceTracker). PCA-APCS-MLR could not effectively distinguish the contributions of different industrial sources in the complex environment of this study, and the contribution of unknown sources was high (average 39.60%). In contrast, the microbial traceability model can accurately identify the contribution of 7 pollution sources and natural sources, effectively reduce the proportion of unknown sources (average of FEAST is 19.81%, SourceTracker is 16.72%), and show better pollution identification and distribution capabilities. FEAST exhibits a more sensitive potential in source apportionment and shorter calculation time than SourceTracker, thus might be used to guide the precise regional pollution control, especially in the complex pollution environments.

Determining priority control toxic metal for different protection targets based on source-oriented ecological and human health risk assessment around gold smelting area

Determining the priority control source and pollutant is the key for the eco-health protection and risk management around gold smelting area. To this end, a case study was conducted to explore the pollution characteristics, source apportionment, ecological risk and human health risk of toxic metals (TMs) in agricultural soils surrounding a gold smelting enterprise. Three effective receptor models, including positive matrix factorization model (PMF), ecological risk assessment (ERA), and probabilistic risk assessment (PRA) have been combined to apportion eco-human risks for different targets. More than 95.0% of samples had a Nemerow pollution index (NPI) > 2 (NPImean=4.27), indicating moderately or highly soil TMs contamination. Four pollution sources including gold smelting activity, mining source, agricultural activity and atmosphere deposition were identified as the major sources, with the contribution rate of 17.52%, 44.16%, 13.91%, and 24.41%, respectively. For ecological risk, atmosphere deposition accounting for 30.8% was the greatest contributor, which was mainly loaded on Hg of 51.35%. The probabilistic health risk assessment revealed that Carcinogenic risks and Non-carcinogenic risks of all population were unacceptable, and children suffered from a greater health risk than adults. Gold smelting activity (69.2%) and mining source (42.0%) were the largest contributors to Carcinogenic risks and Non-carcinogenic risks, respectively, corresponding to As and Cr as the target pollutants. The priority pollution sources and target pollutants were different for the eco-health protection. This work put forward a new perspective for soil risk control and management, which is very beneficial for appropriate soil remediation under limited resources and costs.

Pollution Source Apportionment of Heavy Metals in Cultivated Soil Around a Red Mud Yard Based on APCS-MLR and PMF Models

In order to explore the characteristics and sources of heavy metal pollution in cultivated soil around a red mud yard in Chongqing， the content and spatial distribution characteristics of eight heavy metal elements （Cd， Cr， Hg， Ni， Pb， As， Cu， and Zn） in the soil were analyzed， and the single factor pollution index method and Nemerow comprehensive pollution index method were used to evaluate the characteristics of heavy metal pollution in soil. On the basis of correlation analysis， the APCS-MLR and PMF models were used to quantitatively analyze the sources of heavy metals. The results showed that the average contents of the other seven heavy metal elements were higher than the background values of Chongqing soil， except for that of Cr. The heavy metals Cd， Hg， and As were moderately polluted， and Pb， Cu， Ni， and Zn were mildly polluted. The spatial distribution pattern of Cr， Ni， Pb， Cu， and Zn in the soil was similar， and there was a very significant positive correlation between them （P &lt; 0.01）. The spatial distribution characteristics of Cd， Hg， and As were significantly different， and there was no significant correlation between them （P &gt; 0.05）. The source apportionment showed that the sources of heavy metals in the soil in the study area were relatively complex， and the APCS-MLR and PMF models could identify the same four pollution sources， namely red mud yard percolation emission and natural sources， thermal power generation emission sources， agricultural activities and natural sources， and non-ferrous metal smelting emission sources. There was little difference in the results of source apportionment between the two models. The contribution rates of the four pollution sources in the APCS-MLR model were 51.8%， 18.0%， 15.9%， and 14.3%， respectively， whereas those in the PMF model were 45.9%， 12.8%， 21.5%， and 19.8%， respectively.

Long-term trends and source apportionment of fine particulate matter (PM2.5) and gaseous pollutants in Auckland, New Zealand

Airborne particles and pollutant gases are of increasing concern due to their adverse health effects, necessitating a thorough understanding of their composition, sources, spatial and temporal trends for effective air quality management. This study is part of a source apportionment study in Auckland, a dynamic urban environment with complex air quality challenges in an isolated Southern Ocean setting. Over the 2006–2016 period, concentrations of PM2.5, CO, NO2, and SO2 consistently decreased at all 4 monitoring sites indicating the impacts of control measures. The sources impacting the four sites were identified using the positive matrix factorisation (PMF) receptor model. Common sources affecting these sites included motor vehicles (both petrol and diesel), biomass burning, sea salt, sulphate/marine diesel, and soil/road dust. While motor vehicle emissions and biomass burning emerged as the primary contributors to PM2.5, BC, NO2, CO, and SO2, motor vehicle contributions declined due to advancements in fuel formulation and engine technology despite increased vehicle numbers. Biomass burning contributed substantially to winter PM2.5 concentrations driven by domestic heating practices. However, the introduction of alternative heating technologies mitigated the upward trends in biomass burning emissions despite an increasing residential population. Soil/road dust contributions varied by site, influenced by meteorological conditions and local activities, with implications for site-specific air quality management. Sulphate/marine diesel concentrations exhibited seasonal variability, reflecting the impact of both shipping emissions and natural sources. Urban sulphate concentrations decreased due to regulations requiring the introduction of low-sulphur automotive gasoline and diesel fuels. Sea salt, a naturally occurring source, posed challenges for management efforts with concentrations trending downwards over time, possibly linked to climate patterns. This study's source apportionment analysis provides critical insights into Auckland's air quality. The findings inform policy development, air quality management, and health impact assessments, benefiting both the Auckland region and New Zealand as a whole. Ongoing monitoring and emissions reduction strategies, particularly targeting motor vehicles and biomass burning, are pivotal for enhancing air quality and public health in the region.

Overview of the Trajectory-Ensemble Potential Source Apportionment Web (TraPSA-Web) Toolkit for Atmospheric Pollutant Source Identification

Trajectory ensemble receptor models (TERMs) were widely used to determine the likely source locations and apportionment of air pollutants. This paper describes the development and applications of the Trajectory-ensemble Potential Source Apportionment Web application (TraPSA-Web), a comprehensive toolkit for likely atmospheric pollutant source location apportionments using TERMs and back trajectories generated with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The TERMs integrated within the TraPSA-web include Concentration Field Analysis (CFA), Concentration Weighted Trajectory (CWT), single-site and multiple-site Potential Source Contribution Function (PSCF), and Simplified Quantitative Transport Bias Analysis (SQBA). TraPSA-Web is designed as a web application with a user-friendly modern graphical user interface (GUI), which largely enhances the accessibility to the users. TraPSA-Web will provide the air quality research community with a sophisticated toolkit for (1) easy management of the research project and datasets, (2) efficient automatization for HYSPLIT configurations, calculations, and result aggregations, (3) flexible configurations for the research scenarios and TERM parameters, and (4) interactive visualizations for the pollutant pattern analysis and TERM result mapping.

Advances in Studies on Heavy Metals in Urban Soil: A Bibliometric Analysis

Recent advancements in urban economies and population growth have led to an escalation in urban soil heavy metal pollution, thereby posing significant threats to human life and health. This paper presents a comprehensive bibliometric analysis, detailing the developmental trajectory, research hotspots, and emerging trends in the field of urban soil heavy metal pollution research. Utilizing the CiteSpace and VOSviewer software tools, we conducted an analysis of 1247 articles sourced from the Web of Science Core Collection Database (WoSCC) spanning the period from 2000 to 2022. Our analysis revealed a significant upward trend in the number of publications during the period 2000–2022, a trend expected to persist. Ahmad Kafeel, Khan Zafar Iqbal, and Huang Biao emerged as the leading authors in this domain. The journal, Science of the Total Environment, held the most influence, while China led in the number of publications, with the Chinese Academy of Sciences as the foremost contributor. The research predominantly focused on source apportionment of urban soil heavy metal pollution, pollution risk assessment, and the application of environmental magnetism. Future research priorities include assessing the human health risks from diverse sources of heavy metal pollution, a key aspect of ensuring urban safety and soil health. Our findings delineate the evolutionary trajectory of urban soil heavy metal pollution research over the past two decades and underscore the viability of employing a dynamic bibliometric approach to investigate this research discipline.

Predictions of air quality and challenges for eliminating air pollution during the 2022 Olympic Winter Games

Scientifically and efficiently ensuring good air quality for important events is an issue of concern to the government. In addition to analysis based on historical data, advanced prediction before an event is essential for the government having ample time to take effective actions to improve air quality during the event period. Taking “the 2022 Olympic Winter Games (OWG)” as a typical case, a chemical transport model coupled with a tracer-tagged module was used to evaluate the air quality and source apportionment of ambient pollutants in the OWG host cities under historical and predicted meteorological conditions. Driven by the downscaling of meteorological fields from an operational real-time climate forecast system, the potential ability of air quality forecasting three months ahead was investigated, which was meaningful for designing control strategies. Sensitive simulations indicated that under unfavorable meteorological conditions, such as those during February 2014, both Beijing and Zhangjiakou faced a high risk of experiencing haze episodes, even based on current anthropogenic emission intensity. The contribution of the joint prevention and control region to Beijing and Zhangjiakou would become larger under worse meteorological conditions, which favor heavy air pollution. The source apportionment results indicated that strengthened emission control in cities including Beijing, Zhangjiakou and south of Beijing (Baoding, Langfang, Tianjin, and Tangshan) is effective for reducing haze episodes in the host cities. There is still a long way to make accurate daily fine particulate matter predictions on a seasonal-scale in advance; however, it could capture the trends in air quality in host cities around the OWG period three months ahead. The comparison of observations and predictions confirmed and highlighted the role of regional emission controls on the realization of the “OWG blue”.