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In the early 2000s, the Pearl River Delta (PRD) became one of the earliest regions in China to implement a stringent control policy on air pollution emission. In particular, during the 11th Five Year Plan (FYP) and 12th FYP (i.e., 2006–2015), the emission control measures were executed intensively and efficiently under the supervision of law enforcement authorities. These measures helped substantially reduce the fine particulate matter (PM 2.5) concentration over this region. Hence, it is now important to determine how the decrease in PM 2.5 concentration influenced the exposure condition and health burdens in the PRD region during 2006–2015. In this study, the exposure and mortality apportionment of PM 2.5 under both 2006 and 2015 emission scenarios were investigated. The simulated population-weighted PM 2.5 concentration was found to be lower under the 2015 emission scenario compared with that under the 2006 scenario. The average reductions in simulated PM 2.5 exposure concentrations (population-weighted average) for Guangzhou, Dongguan, Foshan, and Shenzhen were 32.7, 27.0, 25.3, and 24.1 μg/m3, respectively. After excluding the meteorological variations, a difference of approximately 16,400 (95% CI: 9,100, 22,800) in the number of simulated premature deaths was obtained after the 10-year efforts for emission control and industrial restructuring. Among the five major anthropogenic emissions (mobile, area, power plant, marine vessel, and industrial point emissions), the control of mobile emissions was found to be the most relevant to the estimated health benefits. The calculated economic benefits from controlling mobile emissions reached 30,300 (21,600, 37,100) million USD in 2015. In contrast, the mortality related to area emissions turned out to be higher under the 2015 emission scenario. The difference between 2006 and 2015 meteorological scenarios could substantially influence the simulated exposure concentration in each month. However, the impact of the meteorological factors was found to be limited when the exposure concentrations of the 4 months were averaged. Further, 26,700 (18,500, 33,400) premature deaths difference was calculated after the PM 2.5 concentration over the PRD region reached the Air Quality Guideline standard. Therefore, more efforts, including the control of area emissions and the enhancement of regional cooperation (e.g., reducing industrial emission consistently and systematically in southern China), are crucial further to reduce the PM 2.5 concentrations in the PRD region and improve the living conditions for the residents. • Residents of the PRD region were exposed to a much lower PM 2.5 concentration in 2015 than they were in 2006. • The simulated premature mortality difference related to the change in mobile emissions is the largest. • Meteorological variation plays an important role in influencing the PM 2.5 exposure concentration each month. • The calculated health burdens are much lower when the PM 2.5 level in this region meets the WHO guideline level of 10 μg/m3. [ABSTRACT FROM AUTHOR]

Reservoirs play an important role in the urban water supply, yet reservoirs receive an influx of large amounts of pollutants from the upper watershed during flood seasons, causing a decline in water quality and threatening the water supply. Identifying major pollution sources and assessing water quality risks are important for the environmental protection of reservoirs. In this paper, the principal component/factor analysis-multiple linear regression (PCA/FA-MLR) model and Bayesian networks (BNs) are integrated to identify water pollution sources and assess the water quality risk in different precipitation conditions, which provides an effective framework for water quality management during flood seasons. The deterioration of the water quality of rivers in the flood season is found to be the main reason for the deterioration in the reservoir water quality. The nonpoint source pollution is the major pollution source of the reservoir, which contributes 53.20%, 48.41%, 72.69%, and 68.06% of the total nitrogen (TN), phosphorus (TP), fecal coliforms (F.coli), and turbidity (TUB), respectively. The risk of the water quality parameters exceeding the surface water standard under different hydrological conditions is assessed. The results show that the probability of the exceedance rate of TN, TP, and F.coli increases from 91.13%, 3.40%, and 3.34%, to 95.75%, 25.77%, and 12.76% as the monthly rainfall increases from ≤68.25 mm to >190.18 mm. The risk to the water quality of the Biliuhe River reservoir is found to increase with the rising rainfall intensity, the water quality risk at the inlet during the flood season is found to be much greater than that at the dam site, and the increasing trend of TP and turbidity is greater than that of TN and F.coli. The risk of five-day biochemical oxygen demand (BOD 5 ) does not increase with increasing precipitation, indicating that it is less affected by nonpoint source pollution. The results of this study can provide a research basis for water environment management during flood seasons.

The conditions of the sources of heavy metals are essential to assess its potential threats to human health. The identification of the origin of heavy metals is essential for planning effective measures to control long-term accumulation of heavy metals. In this study, analysis of pollution sources was performed on 100 soil samples with geostatistics and absolute principal component score-multiple linear regression (APCS-MLR) receptor model. The descriptive statistics revealed that concentrations of heavy metals (Pb, Zn, Cu, Ni) have exceeded the background value of Zhejiang Province. The coefficient of variation is Pb > Cd > Cu > Zn > Ni > Cr. The APCS-MLR and geo statistical analysis showed that sources of pollution: PC1 was Ni, Cr, Cu, Zn because of soil parent material. The contribution rates were 89.42%, 87.19%, 29.64%, and 33.58%, respectively. The PC2 was Pb, Zn and Cu which were mainly caused by anthropogenic mining activities. The contribution rates were 95.92%, 24.81%, and 40.62%, respectively. The PC3 was Cd、Zn and Cu which was mainly caused by agricultural inputs, and their contribution rates were 91.96%, 41.61%, and 30.14% respectively. According to Nemero Synthesis Index evaluation method, the Shaoxing City Zhejiang, China is heavily polluted with heavy metals. Image 1 • Three sources of soil heavy metals were apportioned by geostatistics and APCS-MLR model. • Pb is mainly derived from anthropogenic mining, while Ni and Cr are mainly derived from the case of soil parent materials. • Zn and Cu are anthropogenic and natural composite pollution. [ABSTRACT FROM AUTHOR]