The dynamics and drivers of environmental performance in Chinese cities: a decomposition analysis

Environmental Science and Pollution Research - This paper examines urban environmental performance using a unique database of 286 Chinese cities for the period 2002–2014. Both the dynamics of...     

武汉地区夏季和秋冬季大气污染的天气概念模型

利用2013～2019年武汉市生态环境局监测数据、L波段雷达探空资料、NCEP/NCAR逐日再分析资料,对夏季和秋冬季武汉地区污染日的大气污染特征、边界层结构、环流形势、物理量场进行研究,建立了武汉地区大气污染的天气概念模型.主要结论如下:(1)武汉市空气质量具有季节性变化特征,大气污染程度四季分布表现为冬＞秋＞春＞夏.夏季首要污染物是臭氧,冬季首要污染物是PM2.5.(2)比较挑选出的夏季清洁日和污染日的气象要素特征,污染日逆温的平均强度约为清洁日的一倍,逆温底高一般在600 m以下,空气质量一般为轻度-中度污染;静风频率(37.1％)明显高于清洁日的静风频率(2.9％);污染日平均风速小(0.8 m/s),边界层内相对湿度较低.同样比较秋冬季两类天气的气象要素特征,污染日逆温底高低、厚度小,不及清洁日的一半,不利于污染物的扩散,易出现重度污染天气.静风频率(20％)高于清洁日的静风频率(7.5％),风速小(1.6 m/s),污染日边界层内呈明显上千下湿的格局.(3)建立了夏季大气污染的天气概念模型,污染日副高偏弱位置偏东,长江流域易少雨干旱;地面我国东部大范围地区处于均压场中,武汉地区为偏东北异常小风,不利于大气污染物的扩散.(4)建立了秋冬季大气污染的天气概念模型,长江流域环流平直少波动,配合地面弱低压的天气形势和较强的逆温使得大气污染物聚集在近地面.蒙古冷高压强度偏弱,使得入侵我国的冷空气强度偏弱;武汉地区为偏北小风,对雾霾的移除和稀释扩散作用差.该研究结论可供大气污染预测预警研究和环境管理部门大气污染的联防联控参考.     

Effects of climate variables on the transmission of COVID-19: a systematic review of 62 ecological studies

Environmental Science and Pollution Research - The new severe acute respiratory syndrome coronavirus 2 was initially discovered at the end of 2019 in Wuhan City in China and has caused one of the...     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Study on coupling between mineral resources exploitation and the mining ecological environment in Shanxi Province

Environment, Development and Sustainability - Due to rapid socioeconomic development, seeking the coordination of resources exploitation and the ecological environment has become an objective...     

Simultaneous removal of cyanobacterial blooms and production of clean water by coupling flocculation with a rotary drum filter

Environmental Science and Pollution Research - A mechanical harvesting technology based on coupling flocculation with a rotary drum filter (RDF, 35-μm) was applied to remove cyanobacterial...     

Probabilistic health risk assessment of nitrosamines in drinking water of Shaoxing,Zhejiang, China

Environmental Science and Pollution Research - Nitrosamines (NAms) are potent genotoxic and carcinogenic but widely detected in drinking water. This study aimed to investigate the occurrence of...     

Sequential coupling of bio-augmented permeable reactive barriers for remediation of 1,1,1-trichloroethane contaminated groundwater

Sequential coupling of high-density luffa sponge (HDLS) immobilized microorganism and permeable reactive barriers (IM Bio-PRBs) was superior to intimate coupling of free microorganism and permeable reactive barriers (FM Bio-PRBs) for remediation of 1,1,1-trichloroethane contaminated groundwater. IM Bio-PRBs had much better performance to removal 1,1,1-trichloroethane (1,1,1-TCA) and prevent the transport of 1,1,1-TCA and inorganic ions (NO₃^?, PO₄^3?, and SO₄^2?). The majority of them were prevented and accumulated in upgradient of IM Bio-PRBs. 1,1,1-TCA and inorganic ions in there contributed to the much faster growth of microorganism in upgradient aquifer. Therefore, the removal of 1,1,1-TCA and consumption of inorganic ions in upgradient of Bio-PRBs played a constructive role in reducing the processing load of following zero-valent iron (ZVI) PRBs and the negative effect of free microorganism cells (biological clogging) and inorganic ions (chemical clogging) on Bio-PRB permeability. In addition, IM Bio-PRBs were more conducive to accelerate the removal of 1,1,1-TCA in long-term remediation and 1,1,1-TCA residual concentration significantly lower than the safety standard of 0.2 mg L^?1. The change of terminal by-products of 1,1,1-TCA contaminated groundwater in Bio-PRBs showed that 1,1,1-TCA could be effectively de-chlorinated and mineralized in Bio-PRBs. The reductant H₂S (prolong the service life of ZVI-PRBs) was much more produced and utilized in IM Bio-PRBs. Taken together, sequentially coupled IM Bio-PRBs had a better overall performance, and its service life could be prolonged. It was a different design and idea to update conventional PRB remediation technology and theory.

     

Boosting freshwater fish conservation with high-resolution distribution mapping across a large territory

The lack of high-resolution distribution maps for freshwater species across large extents fundamentally challenges biodiversity conservation worldwide. We devised a simple framework to delineate the distributions of freshwater fishes in a high-resolution drainage map based on stacked species distribution models and expert information. We applied this framework to the entire Chinese freshwater fish fauna (>1600 species) to examine high-resolution biodiversity patterns and reveal potential conflicts between freshwater biodiversity and anthropogenic disturbances. The correlations between spatial patterns of biodiversity facets (species richness, endemicity, and phylogenetic diversity) were all significant (r = 0.43–0.98, p < 0.001). Areas with high values of different biodiversity facets overlapped with anthropogenic disturbances. Existing protected areas (PAs), covering 22% of China's territory, protected 25–29% of fish habitats, 16–23% of species, and 30–31% of priority conservation areas. Moreover, 6–21% of the species were completely unprotected. These results suggest the need for extending the network of PAs to ensure the conservation of China's freshwater fishes and the goods and services they provide. Specifically, middle to low reaches of large rivers and their associated lakes from northeast to southwest China hosted the most diverse species assemblages and thus should be the target of future expansions of the network of PAs. More generally, our framework, which can be used to draw high-resolution freshwater biodiversity maps combining species occurrence data and expert knowledge on species distribution, provides an efficient way to design PAs regardless of the ecosystem, taxonomic group, or region considered.     

Personal exposure to PM2.5-bound heavy metals associated with cardiopulmonary function in general population

Environmental Science and Pollution Research - To better understand the cardiopulmonary alterations associated with personal exposed PM2.5-bound heavy meals, we conducted a cross-sectional study in...