Pollution Control Strategies Research Articles

Pollution threshold assessment and risk area delineation of heavy metals in soils through the finite mixture distribution model and Bayesian maximum entropy theory

Pollution threshold and high-risk area determination for heavy metals is important for effectively developing pollution control strategies. Based on heavy metal contents in 3627 dense samples, an integrated framework combining the finite mixture distribution model and Bayesian maximum entropy (BME) theory was proposed to assess pollution thresholds, contamination levels and risk areas in an uncertain environment for soil heavy metals. The results showed that the average heavy metal contents were in the order Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, with strong/moderate variation, and the corresponding pollution thresholds were 158.39, 84.29, 47.84, 49.75, 28.95, 18.01, 0.49 and 0.16 mg/kg, respectively. The thresholds were consistently greater than the Zhejiang Province backgrounds but lower than the national risk screening values, except for Cd. Approximately 27.9% of the samples were classified as contaminated at various levels, and they were distributed in the northern, northwestern and eastern regions of the study area. Additionally, 3.73%, 5.34% and 8.22% of the total area were classified as at-risk areas under confidence levels of 95%, 90% and 75%, respectively, through BME theory. The findings provide a reasonable classification system and suggestions for heavy metal pollution management and control.

Atmospheric benzo[a]pyrene in the Yangtze River Delta, China: pollution level and lung cancer risk in 2016 and future predictions.

The Yangtze River Delta (YRD) has undergone widespread polycyclic aromatic hydrocarbon (PAH) pollution. In this study, we simulated the spatial distribution of atmospheric benzo[a]pyrene (BaP, the most carcinogenic PAH) in the YRD in 2016 and 2030 under different emission scenarios using a 3-D atmospheric transport model and evaluated the lung cancer risks posed by BaP during the study years. The purpose of this study is to suggest targeted policy recommendations for policy-makers to mitigate BaP pollution through numerical simulation. Our results showed that the average BaP concentration in the YRD was 0.30ng/m3 in 2016; however, a significant spatial variation was observed, with the highest BaP concentration in Shanghai (0.59ng/m3). The population-weighted incremental lifetime lung cancer risk (PILCR) was 6.67 × 10-6 in 2016, whereas it ranged from 2.70 × 10-6 to 1.05 × 10-5 in 2030 under the five emission scenarios. A higher future population density in the YRD region could increase lung cancer risk. In all scenarios, Shanghai had the highest number of lung cancer cases (range: 208-476). The results suggest that BaP pollution could be effectively improved through the synergistic effect of reducing activity levels and improving technology. Finally, we provide specific suggested pollution control strategies (e.g., accelerating the use of clean energy in rural areas) for atmospheric BaP in the YRD.

Typhoon-associated air quality over the Guangdong–Hong Kong–Macao Greater Bay Area, China: machine-learning-based prediction and assessment

Abstract. The summertime air pollution events endangering public health in the Guangdong–Hong Kong–Macao Greater Bay Area are connected with typhoons. The wind of the typhoon periphery results in poor diffusion conditions and favorable conditions for transboundary air pollution. Random forest models are established to predict typhoon-associated air quality in the area. The correlation coefficients and the root mean square errors in the air quality index (AQI) and PM2.5, PM10, SO2, NO2 and O3 concentrations are 0.84 (14.88), 0.86 (10.31 µg m−3), 0.84 (17.03 µg m−3), 0.51 (8.13 µg m−3), 0.80 (13.64 µg m−3) and 0.89 (22.43 µg m−3), respectively. Additionally, the prediction models for non-typhoon days are established. According to the feature importance output of the models, the differences in the meteorological drivers of typhoon days and non-typhoon days are revealed. On typhoon days, the air quality is dominated by local source emission and accumulation as the sink of pollutants reduces significantly under stagnant weather, while it is dominated by the transportation and scavenging effect of sea breeze on non-typhoon days. Therefore, our findings suggest that different air pollution control strategies for typhoon days and non-typhoon days should be proposed.

New insight into the geochemical mechanism and behavior of heavy metals in soil and dust fall of a typical copper smelter

The desorption mechanism of heavy metals (HMs) in soil around the mining region are complex and affected by multiple pollution sources, including sewage discharge and atmospheric deposition. Meanwhile, pollution sources would change soil physical and chemical properties (mineralogy and organic matter), thus affecting the bioavailability of HMs. This study aimed to investigate the pollution source of HMs (Cd, Co, Cu, Cr, Mn, Ni, Pb, and Zn) in soil near mining, and further evaluate influence mechanism of dust fall on HMs pollution in soil by desorption dynamics processes and pH-dependence leaching test. Result presented that dust fall is the primary pollution source to HMs accumulation in soil. Additionally, the result of mineralogical analysis in dust fall revealed that quartz, kaolinite, calcite, chalcopyrite, and magnetite are the major mineralogical phases by XRD and SEM-EDS. Meanwhile, the abundance of kaolinite and calcite in dust fall is higher than in soil, which is the primary reason of higher acid-base buffer capacity of dust fall. Correspondingly, the weakened or disappeared of hydroxyl after the adding acid extraction (0–0.4 mmol· g−1) demonstrated that hydroxyl is the main participants of HMs absorption in soil and dust fall. These combined findings suggested that atmospheric deposition not only increases the pollution loading of HMs in soil, but also changes the mineral phase composition of soil, which would increase the adsorption capacity and bioavailability of HMs in soil. This is very remarkable that heavy metals in soil influenced by dust fall pollution could be released preferentially when soil pH is changed. The present results of this study would provide efficient and scientific targeted strategies for pollution control of HMs in soil near mining areas.

The underlying mechanisms of PM2.5 and O3 synergistic pollution in East China: Photochemical and heterogeneous interactions

The rapid development of Chinese cities is accompanied by air pollution. Although the implementation of air pollution control strategies in recent years has alleviated PM2.5 pollution, O3 pollution and the synergistic pollution of PM2.5 and O3 have become more serious. To understand the underlying chemical interaction mechanisms between PM2.5 and O3, we applied the modified Weather Research and Forecasting model with Chemistry (WRF-Chem) to study the effects of aerosol-photolysis feedback and heterogeneous reactions on the two pollutants and revealed the contribution of different mechanisms in different seasons and regions in Yangtze River Delta (YRD) in eastern China. We found that, through the aerosol-photolysis feedback, PM2.5 decreased the surface photolysis rates JNO2 and JO1D, resulting in a decrease in O3 concentration in the VOC-sensitive area and a slight increase in the NOx-sensitive area. The heterogeneous reactions reduced O3 concentration in the YRD in spring, autumn and winter by consuming HxOy. While in summer, the heterogeneous absorption of NOx decreased O3 in the NOx-sensitive areas and increased O3 in the VOC-sensitive areas. Heterogeneous reactions also promoted the secondary formation of fine sulfate and nitrate aerosols, especially in winter. Through the combined effect of two chemical processes, PM2.5 can lead to a decrease in O3 concentration of −3.3 ppb (−7.6 %), −2.2 ppb (−4.0 %), −2.9 ppb (−6.3 %), and − 5.9 ppb (−18.7 %), in spring, summer, autumn and winter in YRD. Therefore, if the PM2.5 concentration decreases, the weakening effect of PM2.5 on the ozone concentration will be reduced, resulting in the aggravation of ozone pollution. This study is important for understanding the synergistic pollution mechanism and provides a scientific basis for the coordinated control of urban air pollution.

Coupling coordination relationships between air pollutant concentrations and emissions in China

Clarifying the correlations between air pollutant concentrations and emissions is vital for effective air pollution prevention and control. In this study, we established a coupling coordination degree model to explore the coupling coordination relationships between concentrations and emissions of PM2.5, SO2, and CO from 2013 to 2017. Moran's I value was used to analyze the spatial distributions of coupling and coordination degrees, and seven socioeconomic, meteorological, and ecological indicators were selected to identify the potential influencing factors. The results indicate that the PM2.5 coupling degree decreased from 2013 to 2017, whereas those of SO2 and CO increased. The five-year average coupling degrees were 0.73, 0.71, and 0.63, respectively. The coordination degrees of PM2.5, SO2, and CO all increased during the study period, with five-year averages of 0.43, 0.32, and 0.45, respectively. Both the coupling and coordination degree distributions of all three air pollutants exhibited significant spatial agglomeration characteristics. Generally, the coupling coordination levels of air pollutants in China were high in the east and low in the west. In addition, the levels in the northwest were higher than those in the southwest. Of the influencing factors, nighttime light, road-person ratio, and wind speed were positively correlated with the coupling coordination levels. The normalized difference vegetation index and precipitation was negatively correlated with the coupling coordination levels. These findings can be used to improve the current understanding of correlations between air pollutant concentrations and emissions and provide a reference for decision-makers to formulate more effective air pollution prevention and control strategies.

High Spatial Resolution Emission Inventory of Air Pollutants and Carbon in China’s Independent Coking Industry

China is the largest producer and exporter of coke globally, which means that it is very important to understand the characteristics of air pollutants and carbon emissions from China’s independent coking industry. This study was the first to establish a bottom-up inventory of the air pollutants and carbon emissions of China’s independent coking industry during 2001–2018 based on continuous emission monitoring system online monitoring data and unit-based corporate information. Based on the developed emission inventory, four scenarios were established to analyze potential emissions reduction of air pollutants and carbon dioxide (CO2) in future. The emissions of particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), black carbon (BC) and organic carbon (OC) decreased by 62.11%, 63.41%, 72.85%, 63.41% and 63.41%, respectively. CO2, carbon monoxide (CO), volatile organic compounds (VOCs) and nitrogen oxides (NOX) emissions increased by 355.51%, 355.51%, 355.51% and 99.74%, respectively. In 2018, PM10, PM2.5, SO2, NOx, BC, OC, CO, VOCs and CO2 emissions were, respectively. 45.20, 16.91, 63.84, 117.71, 5.07, 5.92, 554.91, 1026.58 Gg, and 176.88 Tg. Shanxi province made the greatest contributions to the total emissions of air pollutants and CO2 from this industry by 25.01%. The emission source that contributed most to PM2.5 (SO2 and NOX) emissions was coke pushing (quenching and the coke oven chimney respectively) in 2018. Under the ULE scenario (2018–2035), PM2.5 and SO2 emissions will reduce by more than 30%. Under the PCP scenario, PM2.5 and SO2 emissions will reduce by more than 55%. Under the CBP scenario, CO2 emissions will peak at 197.99 Tg in 2025 and decrease to 70% of the peak in 2035. The results showed the emission characteristics of air pollutants and CO2, future emission with several scenarios and cooperative reduction potential in China’s independent coking industry, which provides scientific support for the development of pollution control strategies.

Improvement in water pollution control alters nutrient stoichiometry of Guanting Reservoir near Beijing, North China.

Significant improvement in wastewater treatment is the most effective way for eutrophication control, especially in semiarid regions. However, its effect on the nutrient status and stoichiometry of the receiving water body has remained poorly considered and understood at broad temporal scales. Taking Guanting Reservoir (GR) in Hebei-Beijing (P. R. China) as an example, we present a study that links a continuous monitoring dataset for GR with corresponding estimates of human-induced nutrient discharges in its watershed from the year 2006 to 2019. We find that current GR belongs to strict P limitation and the faster decrease of TP than TN concentrations and continuous increase of TN/TP mass ratios in GR are attributed to the water restoration investment-induced declining of nutrient loadings. The improved municipal wastewater treatment capacity is mainly responsible for these significant changes, due to the higher removal efficiency of TP than TN in municipal wastewater. Given the potential ecological impact on aquatic biodiversity as well as ecosystem function of changes in TN/TP ratios and higher retention rate of TP (97.4%) compared with TN (93.1%) in GR, our findings highlight that future strategy for water pollution control should not only concentrate on more nutrient reduction efficiencies but attach importance to their stoichiometric balance to reduce the potential risk of phytoplankton blooms and toxin production during the water quality recovery of lakes or reservoirs.

Exploring the contributions of major emission sources to PM2.5 and attributable health burdens in China

Ambient fine particulate matter (PM2.5) pollution is the principal environmental risk factor for health burdens in China. Identifying the sectoral contributions of pollutant emissions sources on multiple spatiotemporal scales can help in the formulation of specific strategies. In this study, we used sensitivity analysis to explore the specific contributions of seven major emission sources to ambient PM2.5 and attributable premature mortality across mainland China. In 2016, about 60% of China's population lived in areas with PM2.5 concentrations above the Chinese Ambient Air Quality Standard of 35 μg/m3. This percentage was expected to decrease to 35% and 39% if industrial and residential emissions were fully eliminated. In densely populated and highly polluted regions, residential sources contributed about 50% of the PM2.5 exposure in winter, while industrial sources contributed the most (29–51%) in the remaining seasons. The three major sectoral contributors to PM2.5-related deaths were industry (247,000 cases, 35%), residential sources (219,000 cases, 31%), and natural sources (87,000, 12%). The relative contributions of the different sectors varied in the different provinces, with industrial sources making the largest contribution in Shanghai (65%), while residential sources predominated in Heilongjiang (63%), and natural sources dominated in Xinjiang (82%). The contributions of the agricultural (11%), transportation (6%), and power (3%) sources were relatively low in China, but emissions mitigation was still effective in densely populated areas. In conclusion, to effectively alleviate health burdens across China, priority should be given to controlling residential emissions in winter and industrial emissions all year round, taking additional measures to curb emissions from other sources in urban hotspots, and formulating air pollution control strategies tailored to local conditions.

Characteristics and Risk Assessment of Environmentally Persistent Free Radicals (EPFRs) of PM2.5 in Lahore, Pakistan.

Environmentally persistent free radicals (EPFRs) are an emerging pollutant and source of oxidative stress. Samples of PM2.5 were collected at the urban sites of Lahore in both winter and summertime of 2019. The chemical composition of PM2.5, EPRF concentration, OH radical generation, and risk assessment of EPFRs in PM2.5 were evaluated. The average concentration of PM2.5 in wintertime and summertime in Lahore is 15 and 4.6 times higher than the national environmental quality standards (NEQS) of Pakistan and WHO. The dominant components of PM2.5 are carbonaceous species. The concentration of EPFRs and reactive oxygen species (ROS), such as OH radicals, is higher in the winter than in the summertime. The secondary inorganic ions do not contribute to the generation of OH radicals, although the contribution of SO42+, NO3-, and NH4+ to the mass concentration of PM2.5 is greater in summertime. The atmospheric EPFRs are used to evaluate the exposure risk. The EPFRs in PM2.5 and cigarette smoke have shown similar toxicity to humans. In winter and summer, the residents of Lahore inhaled the amount of EPFRs equivalent to 4.0 and 0.6 cigarettes per person per day, respectively. Compared to Joaquin County, USA, the residents of Lahore are 1.8 to 14.5 times more exposed to EPFRs in summer and wintertime. The correlation analysis of atmospheric EPFRs (spin/m3) and carbonaceous species of PM2.5 indicates that coal combustion, biomass burning, and vehicle emissions are the possible sources of EPFRs in the winter and summertime. In both winter and summertime, metallic and carbonaceous species correlated well with OH radical generation, suggesting that vehicular emissions, coal combustion, and industrial emissions contributed to the OH radical generation. The study's findings provide valuable information and data for evaluating the potential health effects of EPFRs in South Asia and implementing effective air pollution control strategies.

Pollution prevention strategies of SMEs in a green supply chain finance under external government intervention.

Small- and medium-sized enterprises (SMEs) are an important force in the promotion of economic development. However, SMEs in the supply chain are often severely punished by the government for polluting the environment through their production processes. Therefore, it is crucial to solve the environmental pollution problem of SMEs. To compensate for the "common knowledge" assumption of participants in traditional game theory, this study constructs an evolutionary game model of a green supply chain composed of SMEs, core enterprises, and commercial banks. The strategic decisions of the supply chain members are analyzed in relation to external interventions of the government. Subsequently, the impact of government incentives and penalties on these strategic choices is considered. The results show that government incentives can promote SMEs' choice to use pollution control strategies, and government penalties can reduce the risk of an SME defaulting, thus promoting the healthy development of a green supply chain finance system. Furthermore, increasing the new income of SMEs, raising the guarantee rate of core enterprises, and reducing the cost of green loans reviewed by banks and the guarantee cost of core enterprises can effectively promote the equilibrium of the tripartite evolutionary game. These findings indicate that the game model introduced here can effectively solve the environmental pollution problems of SMEs and promote the healthy development of a green supply chain finance system.

Spatio-temporal variations of PM2.5 concentrations and related premature deaths in Asia, Africa, and Europe from 2000 to 2018

Fine particulate matter (PM2.5) pollution is one of leading environmental problems worldwide, posing a serious threat to global public health. Based on long-time series of satellite-retrieved PM2.5 concentrations, this study estimated premature deaths from six diseases (acute lower respiratory infections (ALRI), chronic obstructive pulmonary disease (COPD), type 2 diabetes (DIA), ischemic heart disease (IHD), lung cancer (LNC), and stroke (STR)) attributable to PM2.5 in Asia, Africa, and Europe (AAE). The results showed that the 19-year average population-weighted PM2.5 concentration in AAE was 41.7 μg/m3, with 47.4, 33.9, and 15.5 μg/m3 for Asia, Africa, and Europe, respectively. In AAE, the total annual average premature deaths related to PM2.5 were 2819 thousand (95% confidence interval (CI): 2348–4021), with 2261 thousand (95% CI: 1880–3396) in Asia, 170 thousand (95% CI: 128–276) in Africa, and 388 thousand (95% CI: 305–599) in Europe. STR and IHD were the major contributors to premature deaths in AAE, accounting for 38.5% and 33.3%, respectively. DIA, as a newly considered disease, was responsible for 4.8% premature deaths of the total. Besides, high values of premature deaths were concentrated in China and India during 2000–2018, with 1228 thousand (95% CI: 912–1922) and 506 thousand (95% CI: 389–720) annual average premature deaths (43.6% and 17.9% of the total in AAE). The results of this study provide a reference for different countries in AAE to formulate PM2.5 pollution control strategies.

Spatio-temporal characteristics of PM2.5 and O3 synergic pollutions and influence factors in the Yangtze River Delta

Since the implementation of pollution prevention and control action in China in 2013, particulate pollution has been greatly reduced, while ozone pollution has become gradually severe, especially in the economically developed eastern region. Recently, a new situation of air pollution has emerged, namely, enhanced atmospheric oxidation, ascending regional ozone pollution, and increasing particle and ozone synergic pollution (i.e., double-high pollution). Based on the long-term observation data from 2015 to 2021, we examined the spatio-temporal characteristics of urban PM2.5 and O3 pollution in the Yangtze River Delta and quantified the effects of meteorological and non-meteorological factors on pollution in four city clusters using stepwise multiple linear regression models. Temporally, PM2.5 decreased gradually year by year while, O3 increased in city clusters. Spatially, PM2.5 declined from northwest to southeast, while O3 decreased from northeast to southwest. Except for southern Zhejiang, other city clusters suffer from complex air pollution at different levels. In general, pollution intensity and frequency vary with city location and time. Single PM2.5 pollution mostly occurred in northern Anhui. Single O3 pollution occurred in central and southern Jiangsu and northern Zhejiang. Synergic pollutions of PM2.5 and O3 mainly occurred in central Jiangsu. The contributions (90%) of non-meteorological factors (e.g., anthropogenic emission) to PM2.5 decrease and O3 increase are far larger than that of meteorological factors (5%). Relative humidity, sea level pressure, and planetary boundary layer height are the most important meteorological factors to drive PM2.5 changes during pollution. Downward solar radiation, total cloud cover, and precipitation are the most important meteorological factors that affect O3 changes during pollution. The results provide insights into particulate and ozone pollution in the Yangtze River Delta and can help policymakers to formulate accurate air pollution prevention and control strategies at urban and city cluster scales in the future.

Spatial-temporal characteristics of corrected total phosphorus pollution loads from agricultural non-point sources in Tuojiang River watershed, Sichuan Province of southwestern China.

Traditional method of estimating pollution loads may neglect the internal spatial heterogeneity of socio-economic driving factors, which can result in overestimate and underestimate of pollution loads. In this study, the corrected approach to estimating total phosphorus (TP) pollution load was proposed to explore its future variation to develop effective phosphorus pollution control strategies for water environment management. As the first-class tributary of the Yangtze River, the TP out of limits in the Tuojiang River is serious. Thus, based on the presently related basic datasets related to TP pollution load estimation, we firstly adopted the GM (1,1) model to predict their varied trends from 2021 to 2025. We then used the pollution emission coefficient method to calculate the TP pollution load. Moreover, considering the temporal and spatial heterogeneity of the pollutant generation coefficient, we further introduced population and GDP factors to further modify the pollutant generation coefficient to correct TP pollution load. Finally, we employed the exploratory spatial data analysis (ESDA) method to explore spatial distribution characteristics and spatial autocorrelation of TP pollution load from diverse pollution sources in 2025. The results showed that the total TP pollution load from diverse pollution sources will increase from 12,194.92 t in 2021 to 12,461.26 t in 2025, an increase of 2.18%. More concretely, the TP pollution load from rural domestic sewage, rural domestic waste and livestock, and poultry pollution sources will separately decrease by 94.24 t, 77.9 t, and 86.52 t. However, the TP pollution load from agricultural runoff and agricultural solid wastes pollution sources will increase by 74.52 t and 451.49 t, respectively. The contribution of TP pollution load from diverse pollution sources to total TP pollution load will be as follows: livestock and poultry (63.49%) > agricultural solid wastes (16.72%) > agricultural runoff (12.26%) > rural domestic sewage (4.12%) > rural domestic waste (3.41%). The difference in the spatial distribution of TP pollution load from diverse pollution sources in 2025 will be prominent. TP pollution from rural domestic sewage and rural domestic waste pollution sources is more serious in the Xindu and Longquanyi districts, and that from agricultural runoff and agricultural solid wastes pollution sources is more prominent in the midstream and downstream. TP pollution load from livestock and poultry pollution source is higher in the Renshou, Anyue, Rongxian, Luxian counties, and Jiangyang district. Additionally, TP pollution load from rural domestic sewage, rural domestic waste, agricultural runoff, and agricultural solid wastes pollution sources in 2025 will show a clear spatial correlation, but the spatial correlation of TP pollution load from livestock and poultry pollution source will be weak. The study is effective to eliminate the influence of temporal and spatial variation of pollutants generates coefficients on TP pollution load estimation. The method can reflect the actual condition of pollution loads in watersheds more objectively, which can be applied to estimate other pollution loads of similar watersheds with intensive socio-economic activities. The findings in this study can provide a critical reference for the stakeholders to balance water environment conservation and socio-economic development.

Effects of irrigation on the fate of microplastics in typical agricultural soil and freshwater environments in the upper irrigation area of the Yellow River

Agricultural activities are among the most significant sources of microplastics (MPs) in water. However, few studies have explored the effect of irrigation on the fate of MPs in agricultural systems. This study investigated the distribution of MPs in agricultural soil, surface water, and sediment of adjacent rivers, as well as the “MP communities” in various environments before and after irrigation in a typical agricultural irrigation area of the Yellow River. MPs were detected in all of the examined sites. The number of MPs in surface water and sediment increased after irrigation, whereas those in the surface soil of croplands decreased. In the vertical direction, irrigation accelerated the migration of MPs (< 100 µm) deep into the soil. The vertical mobility of fibers in soil was faster than that of other types of MPs. Moreover, irrigation decreased the correlation between soil properties and MPs in soils. MP community analysis indicated that irrigation enhanced the differences between MP communities among adjacent environments. Collectively, our findings confirmed that river water irrigation caused secondary MP pollution in the soil environment and accelerated MP pollution in deep soil. Therefore, this study provides a theoretical basis for the development of strategies for MP pollution control in agricultural soil.

System dynamics modeling for improving the policy effect of traffic energy consumption and CO2 emissions

In view of the double superposition effect of the continuous increase in the number of motor vehicles, based on the price regulation strategy, in this article, a psychological decision-making algorithm for motor vehicle trips that considers the frame effect and availability heuristic is established to optimize the number of trips made by different vehicle types. Second, a collaborative pollution control and carbon reduction strategy is optimized from the perspective of improving the efficiency of vehicle scrapping and green travel awareness, as well as the efficiency of staggered shifts. The dynamic simulation results show that the collaborative strategy has a ceiling effect on urban traffic pollution control and carbon reduction. Under the 4H-scenario, the degree of PM pollution (pollution control), amount of CO2 generated by motor vehicles (carbon reduction), total energy consumption of passenger cars (energy conservation), and total energy consumption of trucks (energy conservation) decrease by 37.39%, 40.10%, 31.32%, and 55.96%, respectively. In addition, the degree of traffic congestion and the demand for parking spaces decrease by approximately 83.32% and 40.11%, respectively. The results of this study provide a theoretical basis for optimizing the collaborative urban traffic pollution control and carbon reduction strategy.

Air quality improvement through vehicle electrification in Hainan province, China

To improve the ecological environment, provinces in China have set ambitious goals for the electrification of fossil-fuel-powered vehicles (FVs) and the promotion of electric vehicles (EVs). Hainan is the first province to propose a clean energy target that schedules the banning of new FVs sales from 2030. Therefore, Hainan is a good case study to illustrate how this policy might improve regional air quality over the coming years. This study first developed an anthropogenic emission inventory of seven major air pollutants in 2017 in Hainan. The total emissions of CO, NOx, NH3, volatile organic compounds (VOCs), PM10 and PM2.5 and SO2 in 2017 were estimated as 247.56, 69.61, 61.87, 41.38, 37.02, 19.82, and 8.55 kt, respectively. Using the developed emission inventory, multiple scenarios of economic development were considered to assess the benefits to air quality from Hainan's goal of electrification. In comparison with 2017, the reductions in emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, and NH3 by 2045 were projected to be 5.45 (11.11%), 275.07 (57.32%), 675.51 (34.07%), 8.39 (5.73%), 7.73 (8.24%), 81.15 (9.76%), and 4.89 (0.91%) kt, respectively, under the all-electric vehicle scenarios. These results indicate that this policy will not only reduce the emission of air pollutants but also avoid complex O3 pollution in the future. The findings of this work elucidate the effects of vehicle electrification policies on regional air quality and provide scientific support for policymakers in developing pollution control strategies.

Influence of nitrogen inputs, dam construction and landscape patterns on riverine nitrogen exports in the Yangtze River basin during 1980–2015

A quantitative understanding of different N sources and the factors controlling riverine N fluxes is critical for optimizing water pollution control strategies. However, the lack of a comprehensive understanding of N pollution legacy effects and human alteration of landscapes (e.g., dam construction and land use/land cover) hinders development of efficient N management strategies. This study integrated long-term (1980–2015) net anthropogenic nitrogen inputs (NANI) and different forms of N exports, hydroclimate data, dam construction and landscape metrics to elucidate N sources and the factors controlling N fluxes in the Yangtze River basin (YRB). Over the 36-year study period, annual NANI in the YRB increased from 4166 to 8571 kg N km−2 yr−1. Riverine concentrations of total nitrogen (TN), dissolved inorganic nitrogen (DIN) and dissolved organic nitrogen (DON) increased ∼ 3-fold, ∼3-fold and ∼ 36-fold during 1980–2015, respectively, whereas riverine particulate nitrogen (PN) decreased by ∼ 90 %. Such long-term riverine N export dynamics were driven by elevated NANI, landscape configuration change and dam construction activities. Inclusion of these variables in multiple linear regression models explained 91 %, 83 % and 96 % of the annual variability in riverine DIN, DON and PN fluxes, respectively. Estimated riverine TN flux from sum of predicted DIN, DON and PN fluxes showed high agreement with measured values (R2 = 0.92, Nash–Sutcliffe coefficients = 0.91), indicating strong efficacy for the developed regression models. Separation of annual riverine N exports into current year NANI versus legacy source contributions indicated that legacy sources contributed ∼ 49 % (26–82 %), 51 % (<1–78 %), 95 % (87–98 %) and 56 % (36–86 %) of riverine DIN, DON, PN and TN exports, respectively. The release of legacy N from the landscape is associated with increases in dam construction, urban density, and the number of small area farm operations between 1980 and 2015. This study highlights the potential of aggregating multiple historical N input datasets, along with changes in land use and river regulation to model long-term riverine N export dynamics. The results of the study provide a foundation for developing integrated watershed N management strategies that address both current and legacy N pollution.

Quantifying the Spatiotemporal Heterogeneity of PM2.5 Pollution and Its Determinants in 273 Cities in China.

Fine particulate matter (PM2.5) pollution brings great negative impacts to human health and social development. From the perspective of heterogeneity and the combination of national and urban analysis, this study aims to investigate the variation patterns of PM2.5 pollution and its determinants, using geographically and temporally weighted regression (GTWR) in 273 Chinese cities from 2015 to 2019. A comprehensive analytical framework was established, composed of 14 determinants from multi-dimensions, including population, economic development, technology, and natural conditions. The results indicated that: (1) PM2.5 pollution was most severe in winter and the least severe in summer, while the monthly, daily, and hourly variations showed "U"-shaped, pulse-shaped and "W"-shaped patterns; (2) Coastal cities in southeast China have better air quality than other cities, and the interaction between determinants enhanced the spatial disequilibrium of PM2.5 pollution; (3) The determinants showed significant heterogeneity on PM2.5 pollution-specifically, population density, trade openness, the secondary industry, and invention patents exhibited the strongest positive impacts on PM2.5 pollution in the North China Plain. Relative humidity, precipitation and per capita GDP were more effective in improving atmospheric quality in cities with serious PM2.5 pollution. Altitude and the proportion of built-up areas showed strong effects in western China. These findings will be conductive to formulating targeted and differentiated prevention strategies for regional air pollution control.

Understanding Responses of Atmospheric Pollution and its Variability to Contradicting Nexus of Urbanization–Industrial Emission Control in Haldia, an Industrial City of West Bengal

Poor atmospheric health has been causing millions of deaths globally by attacking the respiratory system. According to the World Health Organization, India has the most heavily polluted cities overall and contributes significantly to the annual particle concentration at a global level. This situation demands intensive and extensive monitoring systems for strategy optimization at various levels of governance to combat health and environmental. These strategies include an efficient air quality control strategy, the management of pollution sources while taking into account both local and national controls, etc. However, inadequacy in monitoring stations, which are primarily targeted at cities, has kept most of the small towns and villages out of the radar. This poses difficulty in assessing pollution levels at the local and regional scales, thereby causing a poor understanding of the intensity and spatio-temporal variability of pollutant concentration. Addressing this issue, an attempt has been made in this study to understand the atmospheric pollution of an industry-based city, Haldia in West Bengal, where all the monitoring stations are situated in industrial centers and function nearby making a larger part unmonitored. Handheld instruments were used to collect field data for five other stations selected mainly from non-industrial areas experiencing rapid detrimental land use conversion. Despite being spatially consistent in higher (2015–2019 mean of 95) and lower Air Quality Index (2015–2019 mean of 65), respectively, in industrial and non-industrial areas, contradicting temporal trend is quite distinct in them. Pollution has increased in areas experiencing rapid LULC change, especially in rapidly urbanized areas, whereas industrial emission controls and implementation of various pollution control strategies, including implementation of pollution base profiling and baseline policy action, graded response action plan, etc., have been successful in abating pollution in areas near industries. The study pinpoints the requirement of spatially consistent awareness in rapidly urbanized sections of Haldia to avoid possible environmental hazards caused by urbanization and the necessity of having a dense network of monitoring stations to capture local variations in pollutant concentrations for a better understanding of atmospheric health.