A comparison analysis of the decoupling carbon emissions from economic growth in three industries of Heilongjiang province in China

Environmental Science and Pollution Research - Growing environmental pressure urges China to develop in a sustainable and low carbon way, and thus China strives to achieve a carbon peak by 2030 and...     

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.     

Protective effects of dietary betaine on intestinal barrier function and cecal microbial community in indigenous broiler chickens exposed to high temperature environment

Environmental Science and Pollution Research - High temperature environment causes reduction in productivity in broilers by disrupting the intestinal barrier function. This study aimed to...     

Could biochar amendment be a tool to improve soil availability and plant uptake of phosphorus? A meta-analysis of published experiments

Environmental Science and Pollution Research - As one of the most important nutrients for plant growth, phosphorus was often poorly available in soil. While biochar addition induced improvement of...     

Association between air pollution and COVID-19 infection: evidence from data at national and municipal levels

Environmental Science and Pollution Research - The impact of high concentrations of air pollution on COVID-19 has been a major air quality and life safety issue in recent studies. This study aimed...     

Improved product selectivity of electrochemical reduction of carbon dioxide by tuning local carbon dioxide concentration with multiphysics models

Environmental Chemistry Letters - Electrochemical reduction of carbon dioxide (CO2) is promising to alleviate carbon emissions and produce fuels and materials in a circular way, yet effective...     

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.     

The pollution halo effect of technology spillover and pollution haven effect of economic growth in agricultural foreign trade: two sides of the same coin?

Environmental Science and Pollution Research - The existing literature on the environmental Kuznets curve (EKC) fails to investigate the spatial attribute of the “pollution halo” effect...     

Combination with catalyzed Fe(0)-carbon microelectrolysis and activated carbon adsorption for advanced reclaimed water treatment: simultaneous nitrate and biorefractory organics removal

A process combining catalyzed Fe(0)-carbon microelectrolysis (IC-ME) with activated carbon (AC) adsorption was developed for advanced reclaimed water treatment. Simultaneous nitrate reduction and chemical oxygen demand (COD) removal were achieved, and the effects of composite catalyst (CC) addition, AC addition, and initial pH were investigated. The reaction kinetics and reaction mechanisms were calculated and analyzed. The results showed that CC addition could enhance the reduction rate of nitrate and effectively inhibit the production of ammonia. Moreover, AC addition increased the adsorption capacity of biorefractory organic compounds (BROs) and enhanced the degradation of BRO. The reduction of NO₃^?–N at different pH values was consistently greater than 96.9%, and NH₄⁺–N was suppressed by high pH. The presence of CC ensured the reaction rate of IC-ME at high pH. The reaction kinetics orders and constants were calculated. Catalyzed iron scrap (IS)-AC showed much better nitrate reduction and BRO degradation performances than IS-AC and AC. The IC-ME showed great potential for application to nitrate and BRO reduction in reclaimed water.

     

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...