Spatial Variability and Monitoring of Pb Contamination of Farming Soils Affected by Industry

In this study, the relationship between some physico-chemical properties of soils and lead contamination in soil due to emission from industrial operations in Samsun province of Turkey was investigated. The extent of timely contamination was studied by comparing the obtained results with the results of the study conducted in the same region in 1998. An area of 225 km² (15 km × 15 km), which was divided into 1000 × 1000 m grid squares (16 lines in the east and south directions), was selected within the industrial area. The total of 256 grid points was obtained and soil samples were collected from three depths (0–5, 5–15, and 15–30 cm) of each grid center in 2004. The total Pb concentrations of soil samples were determined as 65.84–527.04 μg g⁻¹ at 0–5 cm in depth, 58.50 – 399.54 μg g⁻¹ at 5–15 cm in depth, and 44.65–330.07 μg g⁻¹ at 15–30 cm in depth. DTPA-extractable Pb concentrations of soils were found to be in the range of 1.52–9.03 μg g⁻¹, 0.54–7.09 μg g⁻¹, 0.19–6.13 μg g⁻¹ at 0–5, 5–15, and 15–30 cm depths, respectively. There were significant relationships between both total or DTPA-extractable Pb concentrations and selected physico-chemical properties of soil. According to enrichment factor (EF) values calculated from the total Pb concentrations, 11.3% of the study area (225 km²) was enriched with Pb in high level, but 77% of the area was in significant enrichment level with Pb. The average total and DTPA-extractable Pb concentrations increased as 11 and 13%, respectively in comparison with the results of 1998.     

The impacts of climate change on the risk of natural disasters

Human emissions of greenhouse gases are already changing our climate. This paper provides an overview of the relation between climate change and weather extremes, and examines three specific cases where recent acute events have stimulated debate on the potential role of climate change: the European heatwave of 2003; the risk of inland flooding, such as recently in Central Europe and Great Britain; and the harsh Atlantic hurricane seasons of 2004 and 2005. Furthermore, it briefly assesses the relation between climate change and El Ni?o, and the potential of abrupt climate change. Several trends in weather extremes are sufficiently clear to inform risk reduction efforts. In many instances, however, the potential increases in extreme events due to climate change come on top of alarming rises in vulnerability. Hence, the additional risks due to climate change should not be analysed or treated in isolation, but instead integrated into broader efforts to reduce the risk of natural disasters.     

排放源烟道气二噁英空间分布对监测结果的影响

针对生活垃圾焚烧企业烟气排放烟囱6个断面单点采样的试验结果,分析按照《固定污染源排气中颗粒物测定与气态污染物采样方法》(GB/T 16157—1996)多断面点采样与单一固定断面点采样对监测结果的影响。试验表明,烟道气在不同断面点质量浓度分布差异较大,相对于样品实际监测值,各断面点二噁英毒性当量浓度最大正偏差达到+306%,最大负偏差达到-96%,测定的18个断面点样品,只有2个样品与实际样品监测值相近。     

固城湖春季水环境因子空间变异及其原因探讨

在现场采样数据的基础上,运用Arc GIS的地统计学组件分析了固城湖春季水环境因子空间变异规律,并从污粢源分布、水生植物密度差异方面探讨了水环境因子空间变异产生的原因.结果表明:(1)变异函教选择球状模型和高斯模型拟合水环境因子的变差曲线,从预测误差判断,拟合效果很好,因此,固城湖水环境因子的空同分布图比较真实地反映了固城湖水环境现状;(2)固城湖TN、NH4+-N基本上呈现从南到北逐渐升高的趋势,TN、NH4+-N、SD的空间变异主要是由人类活动引起,水革对TN、NH4+-N的吸收作用、SD的改善作用不明显;(3)太湖区TP基本上呈现从中西部向东逐渐升高的趋势,与大湖区水草密度的变化趋势相反;水革密度越大(大湖中南部)pH越高,而水草密度越小(航道、湖东部)pH越小;(4)中南部湖区围罔的全部拆除、港口河上游生活污水减少,都使得中南部湖区水质得到改善.     

滆湖大型底栖动物群落分布和氮磷因子的相关分析

于2012年12月—2013年11月对滆湖水体进行了调查,分析了水体氮、磷及大型底栖动物的时空分布,初步探讨大型底栖动物群落结构与氮、磷环境因子的关系。结果表明,滆湖水体的ρ(TN)在冬春季较高,ρ(TP)四季的差异不大,在空间分布上,北部湖区的ρ(TN)、ρ(TP)均高于南部湖区;大型底栖动物出现频率最高的为霍甫水丝蚓和中国长足摇蚊,其丰度在时间上均表现为秋季夏季冬季春季,在空间上的规律性不明显;CCA分析表明滆湖大型底栖动物群落结构随季节变化规律显著,且氮含量是滆湖底栖动物群落结构的重要影响因子;对优势种的丰度与氮磷的相关性分析结果显示,滆湖底栖动物优势种与TN和NO_3~--N有显著的负相关关系。     

珠海市PM_(2.5)时空分布特征及其成因分析

选取2015年珠海市国控监测站ρ(PM_(2.5))数据,分析PM_(2.5)中有机碳(OC)、元素碳(EC)、水溶性离子组分等化学组成,ρ(PM_(2.5))时空分布特征,以及与气象因素的相互关系。结果表明,2015年珠海市PM_(2.5)年均值为31.0μg/m3,表现出显著的时间分布规律,月均值呈现"V"型趋势,PM_(2.5)中主要化学组分是有机物(OM),占总质量的34.0%,其次是硫酸根(SO2-4),占总质量的26.9%,具有明显的季节分布特征,呈现冬高夏低分布;ρ(PM_(2.5))日变化呈现双峰型分布,其值工作日显著高于非工作日;ρ(PM_(2.5))与平均温度、相对湿度、风速呈现负相关关系,与气压呈现显著正相关关系;珠海市ρ(PM_(2.5))空间分布总体呈现"东高西低,北重南轻"变化趋势,有机物、SO2-4和NH+4空间分布呈现东部高于西部趋势,颗粒物浓度受地形、气候因素和海域环境等影响呈现多样化分布趋势。     

济南市夏季甲醛时空分布特征及对臭氧污染的影响

基于Sentinel-5P卫星遥感数据,分析济南市2019—2021年夏季甲醛浓度的时空分布特征及对臭氧污染的影响。结果表明：研究期间夏季甲醛平均柱浓度呈逐年下降趋势,2020年同比降幅最大为24%;甲醛浓度高值区域主要分布在人口密集的市区及工业聚集的章丘区、济阳区和商河县,呈现由城市中心向外扩散的趋势;甲醛浓度受周边城市的影响,形成一条东西向的区域化污染带;甲醛对臭氧的影响主要表现在臭氧污染轻度或污染初期,而氮氧化物的排放进一步加重臭氧污染。     

深圳市酸雨时空变异特征

根据深圳市2009—2013年酸雨监测数据,统计并分析深圳市酸雨强度与频率、时空变异特征和降水c(SO2-4)/c(NO-3)比值特征变化。结果表明:雨量的大小及其出现次数与酸雨出现次数呈负相关;2009—2013年间6个行政区酸雨量呈现"V"形变化趋势,且2011年达到最低值;酸雨危害较严重的是盐田区、南山区和福田区,其次是宝安区和龙岗区,危害较轻的是罗湖区。     

基于SDSS的水环境决策系统技术研究

空间决策支持系统(SDSS)作为地理信息系统与决策支持系统的结合体,兼有两者优点。文章将空间决策支持技术引入水环境计算中,讨论如何将水环境预测模型、决策分析方法与空间分析技术进行有机的结合,分析水环境空间决策模型的结构和建立过程,并结合应用实例——突发性水污染事故应急系统为例,阐明了其应用模型的特点,最后给出了相应的建设性结果。     

Influence of urban spatial and socioeconomic parameters on PM2.5 at subdistrict level: A land use regression study in Shenzhen, China

The intraurban distribution of PM_2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM_2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM_2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R² compared with cross-validation results. For MLR models, inflation of both R² and R²_CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM_2.5 levels. Inflated within-sample R² also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM_2.5 concentrations.