基于污染物通量的潘家口—大黑汀水源地水污染生态补偿量化研究

回顾我国水污染补偿的研究及实践,分析潘家口—大黑汀水源地及入库河流的水质状况,在明确考核断面及考核指标的基础上,建立基于污染物通量的水污染补偿量化估算模型,并根据2013年潘家口—大黑汀水源地入库河流的水质和水量监测数据,估算入库河流水污染生态补偿量。

基于BP人工神经网络平潭海域赤潮叶绿素a浓度模型演算研究

以福建平潭海域为研究对象、以叶绿素a浓度为输出指标,根据2009-2018年赤潮期数据规律及2013-2017年海洋监测数据主成分分析结果,对拟构建的BP模型进行输入指标筛选,选定结果包括4个气象因子和4个水质因子。基于此结果,对2013-2017年的698组海洋监测数据中叶绿素a浓度进行归一化处理并进行模型演算,随机选取80%数据作为演算模型的训练样本,其余进行模型验证。通过交叉变换输入指标,寻求最优的输入节点组合,以气温、溶解氧浓度、日照时长指标为输入参数时,BP模型误差较小(均方根误差为0.05μg/L,平均绝对误差为0.03μg/L),演算结果精度较高(可决系数R^2=0.81)。以上结果表明,气温、溶解氧浓度和日照时长对叶绿素a浓度表征效果较好,可为平潭海域以叶绿素a浓度作为判定指标的赤潮预警研究提供参考。

东张水库蓝藻水华BP人工神经网络模型演算研究

本文以福建省福清市东张水库为例,采集2016—2017年间包含水华期间在内的共295组的水质(水温、pH、电导率、浊度、溶解氧)和气象(气温、风速)数据,以80%的数据进行模型演算,20%的数据进行模型验证,以叶绿素a浓度作为输出参数,应用BP人工神经网络模型进行演算。通过输入不同的参数组合,将结果与实际测定的叶绿素a值比较,挑选出最优的参数组合。结果表明,当以水温、溶解氧、电导率和气温作为组合变量输入时,输出的结果最优,输出数据的预测值与实测值拟合度R2为0. 83,均方根误差(RMSE)为0. 08μg/L,均方根-实测值标准偏差比(RSR)为0. 43,且模型稳定性较好。表明该参数组合作为输入参数建立的BP人工神经网络预警模型,有望未来用于预测东张水库富营养化的发生。

Heavy Metal Fluxes in Tropical Urban Forest Soil in Abidjan District (Côte d’Ivoire)

In Western Africa, the growth of cities has led to natural resource pollution, especially air pollution. Urban forests play a key role in filtering atmospheric particles and pollutants through the canopy before reaching the soil. This study aims to quantify heavy metal fluxes in an urban forest in the district of Abidjan in order to assess its role in the protection of natural resources. A monitoring of wet deposition (throughfall and open field rain) and litterfall was carried out for six months in the urban forest of the National Floristic Center located in Abidjan, C?te d’Ivoire. The results show that the soil of this urban forest is a ferralsol type characterized by a sandy-clay texture and a low load of coarse elements. The annual litterfall is estimated to 12.16 ± 0.71 t·ha-1·yr-1, similar to other tropical forests. Annual quantities of rain and throughfall are in the range of the rainfall recorded in the district of Abidjan (2013 ± 152 and 1773 ± 51 mm). Chemical analyses showed that litter and rainfall contain Mn, Zn, Ni, Cr, Cd and Hg. Manganese and Zn are the most abundant elements and Hg the least abundant in both rainfall and litter. The main source of input of the heavy metals into the urban forest soil is associated with biological recycling through the litter. The litterfall contributes to metal fluxes in soil 109 times greater than metal fluxes carry by wet depositions (open field rain and throughfall). However, a detailed study of rainfall showed that the forest canopy constitutes a barrier for the transfer of heavy metal to urban soil. This is indicated by a decrease in heavy metal content from open field rain to throughfall. Consequently, this study recommends the creation and maintenance of urban forests to increase biomass canopy and improve atmospheric air quality for West African cities undergoing constant change and development.

Time Series Analysis and Forecasting of the Air Quality Index of Atmospheric Air Pollutants in Zahleh, Lebanon

During the last decades, air pollution has become a serious environmental hazard. Its impact on public health and safety, as well as on the ecosystem, has been dramatic. Forecasting the levels of air pollution to maintain the climatic conditions and environmental protection becomes crucial for government authorities to develop strategies for the prevention of pollution. This study aims to evaluate the atmospheric air pollution of the city of Zahleh located in the geographic zone of Bekaa. The study aims to determine a relationship between variations in ambient particulate concentrations during a short time. The data was collected from June 2017 to June 2018. In order to predict the Air Quality Index (AQI), Naïve, Exponential Smoothing, TBATS (a forecasting method to model time series data), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were implemented. The performance of these models for predicting air quality is measured using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Relative Error (RE). SARIMA model is the most accurate in prediction of AQI (RMSE = 38.04, MAE = 22.52 and RE = 0.16). The results reveal that SARIMA can be applied to cities like Zahleh to assess the level of air pollution and to prevent harmful impacts on health. Furthermore, the authorities responsible for controlling the air quality may use this model to measure the level of air pollution in the nearest future and establish a mechanism to identify the high peaks of air pollution.

Multivariate Spatial and Temporal Analysis to Study the Variation of Physico-Chemical Parameters in Litani River, Lebanon

Water quality of Litani River was deteriorated due to rapid population growth and industrial and agricultural activity. Multivariate analysis of spatio-temporal variation of water quality is useful to improve the projects of water quality management and treatment of the river. In this work, analysis of samples from different locations at different seasons was investigated. The spatio-temporal variation of physico-chemical parameters of the water was determined. A total of 11 water quality parameters were monitored over 12 months during 2018 at 3 sites located in different areas of the river. Multivariate statistical techniques were used to study the spatio-temporal evolution of the studied parameters and the correlation between the different factors. Principal Component Analysis (PCA) was applied to the responsible factors for water quality variations during wet and dry periods. The multivariate analysis of variance (MANOVA) was also applied to the same factors and gives the best results for both spatial and temporal analysis. A black point of agricultural, industrial and sewage water pollution was identified in Jeb-Jennine station from the high concentrations of ammonia, sulfate and phosphate. This difference was proved by the major changes in the values of the parameters from one station to the other. Jeb-Jennine represents a main pollution area in the river. The high ammonia, sulfate and phosphate concentrations result from the important agricultural, industrial and sewage water pollution in the area. A high bacterial activity was highlighted in Jeb-Jennine and Quaroun stations because of the presence of the high nitrite concentrations in the two locations. All parameters are highly affected by climate factors, especially temperature and precipitation. TDS, salinity, electrical conductivity and the concentrations of all pollutants increase during wet season affected by the runoff. Other factors can affect the water quality of the river for example geographical features of the region and seasonal human activity like tourism. The correlation between different parameters was evaluated using PCA statistical method. This correlation is not stable, and evolves between wet and dry season.

Chloramination of wastewater effluent: Toxicity and formation of disinfection byproducts

The reclamation and disinfection of waters impacted by human activities（e.g., wastewater effluent discharges） are of growing interest for various applications but has been associated with the formation of toxic nitrogenous disinfection byproducts（N-DBPs）. Monochloramine used as an alternative disinfectant to chlorine can be an additional source of nitrogen in the formation of N-DBPs. Individual toxicity assays have been performed on many DBPs, but few studies have been conducted with complex mixtures such as wastewater effluents. In this work, we compared the cytotoxicity and genotoxicity of wastewater effluent organic matter（Ef OM） before and after chloramination. The toxicity of chloraminated Ef OM was significantly higher than the toxicity of raw Ef OM, and the more hydrophobic fraction（HPO）isolated on XAD-8 resin was more toxic than the fraction isolated on XAD-4 resin.More DBPs were also isolated on the XAD-8 resin. N-DBPs（i.e., haloacetonitriles or haloacetamides） were responsible for the majority of the cytotoxicity estimated from DBP concentrations measured in the XAD-8 and XAD-4 fractions（99.4% and 78.5%, respectively）.Measured DBPs accounted for minor proportions of total brominated and chlorinated products, which means that many unknown halogenated compounds were formed and can be responsible for a significant part of the toxicity. Other non-halogenated byproducts（e.g.,nitrosamines） may contribute to the toxicity of chloraminated effluents as well.