区域PM2.5时空回归建模与预测

基于区域PM_(2.5)时空建模和预测的需要及PM_(2.5)浓度呈现明显的时空分布趋势的状况,以苏南地区2014年PM_(2.5)日监测数据为实验数据,使用回归克里格对区域PM_(2.5)进行时空建模和估值。利用最小二乘法建立了PM_(2.5)与时空位置的三元二次回归趋势模型,建模点趋势值与实测值间的平均误差接近于0,表明趋势模型拟合效果较好;拟合了样点残差的理论变异函数模型,表明该地区PM_(2.5)的空间和时间相关性范围分别为150 km和4 d;基于该模型,使用时空普通克里格对残差进行时空插值;插值结果与趋势项相加,得到PM_(2.5)回归克里格估值结果;通过对比不考虑趋势的时空普通克里格估值结果,发现考虑时空趋势的时空回归克里格法精度提高了1. 29%。对所提方法进行了创新性分析,并对不足之处进行了讨论。     

基于遥感数据的京津冀地区PM2.5时空分布特征

基于MODIS AOD遥感数据,采用多元线性回归模型对PM2.5地面监测数据进行模拟估算,同时加入降水量、相对湿度等气象因子以提高模型精度,结合GIS空间分析技术,得到2015—2016年京津冀地区空间连续的PM2.5浓度分布。结果表明:利用多元线性回归模型反演PM2.5浓度效果较好,R 2均在0.59~0.84之间。在时间上,京津冀地区PM2.5浓度呈现出夏季最低、秋季稍高、冬春两季最高的变化趋势;在空间上,2015年和2016年京津冀地区PM2.5浓度有明显的区域差异,均呈现出西北低、东南高的分布格局,大致与燕山山脉和太行山脉走向一致。     

江苏省PM_(2.5)质量浓度的时空变化格局模拟

基于遥感数据,利用多元线性回归模型研究地面监测的PM_(2.5)质量浓度数据与AOD、气象数据及地面植被覆盖等数据的关系,空间精细化反演江苏省PM_(2.5)质量浓度分布。结果表明,AOD、气象数据及地面植被覆盖数据能较好地反演出PM_(2.5)质量浓度时空分布特征;江苏全省PM_(2.5)质量浓度呈现出冬高秋低、春夏居中的季节变化规律;春、冬季PM_(2.5)质量浓度的高值区集中在苏锡常、宁镇扬及泰州、南通等东南沿海的城市,而在靠近西北内陆的盐城、连云港、徐州、淮安、宿迁PM_(2.5)质量浓度较低,夏、秋季呈现出相反的态势。     

新疆大气颗粒物的时空分布特征

基于2015年新疆12个城市的PM_(10)和PM_(2.5)地面监测数据,并结合同期气象观测数据,分析了新疆PM_(10)和PM_(2.5)质量浓度的时空分布特征及其与气象要素的关联性。结果表明:新疆大气颗粒物年均质量浓度呈现南高北低特征,南疆各城市的年均PM_(10)质量浓度为150～262μg/m~3,年均PM_(2.5)质量浓度为50～118μg/m~3。北疆各城市的质量浓度相对较低,年均PM_(10)质量浓度为28～139μg/m~3,年均PM_(2.5)质量浓度为13～74μg/m~3。从时间变化来看,在春季和夏季,新疆以粗颗粒物污染为主,冬季以细颗粒物污染为主。此外,在南疆各城市3月PM_(10)质量浓度突然大幅升高与相对湿度明显下降、风速增大直接相关,沙尘天气是导致该区域春季高PM_(10)质量浓度的重要原因。     

长沙市大气中PM_(2.5)浓度分布的空间插值方法比较

采用普通克里金(Kriging)、反距离加权(IDW)、趋势面、协同克里金(Co-kriging)插值和基于反距离加权的克里金插值方法对长沙市某时刻的PM_(2.5)浓度空间插值比较,结果表明:反距离加权和趋势面插值的结果与实际情况相差较远;普通克里金和协同克里金插值虽能反映出PM_(2.5)浓度分布一定的空间结构,但精度有待提高,且运行速度较慢;前4种方法效果不好的主要原因是由于长沙市PM_(2.5)浓度监测站点稀少。而基于反距离加权的克里金插值方法结合了2种模型的优点,其结果较为准确地分析长沙市PM_(2.5)浓度分布特征,误差远小于其他方法,且具有平滑性好、运行速度相对较快等优点。     

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。     

石家庄市空气颗粒物污染与气象条件的关系

利用2013—2014年石家庄市环境监测中心PM_(2.5)、PM_(10)逐时监测资料、同期的石家庄市地面气象观测站常规观测资料以及环境监测梯度站2013年1月各层PM_(2.5)和PM_(10)逐时观测资料,分析了PM_(2.5)、PM_(10)质量浓度的时空分布特征及与气象要素的相关关系。结果表明:石家庄市PM_(2.5)与PM_(10)的质量浓度及两者的比值均为冬季和秋季较高;在水平分布上,PM_(2.5)与PM_(10)的平均质量浓度为市区西部高于东部;在垂直分布上,随着高度的增加,PM_(2.5)和PM_(10)平均质量浓度先上升后下降;PM_(2.5)与PM_(10)的质量浓度与相对湿度呈正相关,其中PM_(2.5)的质量浓度与相对湿度相关性更高;PM_(2.5)与PM_(10)的质量浓度与风速呈负相关,随着风速的增大,PM_(2.5)与PM_(10)的平均质量浓度呈下降的趋势,但当风速大于5 m/s时,PM_(10)的质量浓度随着风速增大而上升,出现扬尘污染,总体来讲,刮西北风时PM_(2.5)与PM_(10)的质量浓度较高,刮东南风时PM_(2.5)与PM_(10)的质量浓度较低,这与风向和风速的日变化有关;PM_(2.5)与PM_(10)的质量浓度与降水呈负相关,随着降水的增加,PM_(2.5)与PM_(10)的平均质量浓度呈下降的趋势。     

上海市浦东新区PM_(2.5)中多环芳烃的时空分布与风险评估

通过对浦东新区9个点位PM_(2.5)中的多环芳烃为期1 a的采样分析,获得浦东新区PM_(2.5)中PAHs的时空变化特征。监测表明,冬季PAHs的浓度为夏季的7.9倍,空间上南部偏高;虽然不同季节不同环数的PAHs浓度变化存在一定差异,但均为5~6环占比最大,其次为4环,2~3环占比最少。结合PAHs呼吸致癌风险评估,浦东新区PAHs致癌风险值分布与实际肺癌发病率分布在冬季具有相关性。     

西宁市城区冬季PM2.5和PM10中有机碳、元素碳污染特征

2014年11月—2015年1月对西宁市冬季开展PM_(2.5)和PM_(10)的连续监测。利用DRI 2001A型热光碳分析仪(美国)对有机碳和元素碳进行分析,结果表明:西宁市冬季PM_(2.5)和PM_(10)中碳气溶胶所占比例分别为33.13%±6.83%、24.21%±6.27%,说明碳气溶胶主要集中在PM_(2.5)中;OC/EC值均大于2,说明西宁市大气中存在二次污染;SOC占PM_(2.5)和PM_(10)的质量浓度比例分别为46.50%和57.40%,PM_(2.5)中SOC浓度占PM_(10)中SOC浓度的61.88%,说明SOC主要存在于PM_(2.5)中,且SOC形成的二次污染和直接排放的一次污染都是西宁市碳气溶胶的主要来源;与其他城市比较发现,西宁市冬季PM_(2.5)中的碳气溶胶含量普遍高于其他城市,PM_(10)中OC质量浓度相对其他城市较高,EC质量浓度偏低;OC和EC的相关性不显著,说明来源不统一;进一步对OC和EC各组分质量浓度进行分析知,西宁市冬季碳气溶胶主要来源于机动车汽油排放、燃煤和生物质燃烧。     

常州市国控站PM_(2.5)监测的代表范围研究

为评价常州市国家大气自动监测站(国控站)细颗粒物(PM_(2.5))监测的代表范围,通过1 km×1 km的多角度大气校正算法(MAIAC)的气溶胶光学厚度(AOD)等相关数据,采用随机森林方法进行PM_(2.5)估算反演,考虑变异函数和最优分割模型等统计学模型,开展国控站PM_(2.5)监测代表范围的评价。结果表明:(1)估算反演的PM_(2.5)浓度空间分布显示,常州市区东部区域浓度相对较高,西南部区域浓度相对较低;(2)变异函数分析中,PM_(2.5)浓度在5 km范围内具有相对显著的空间相关性,但超过5 km范围后空间自相关性不显著,差异性增大到最大;(3)最优分割法分析中,常州市各个国控站PM_(2.5)监测均存在各自的代表范围,2019年的代表范围为3~5 km,其中“经开区”站点范围最大(5 km),“市监测站”和“武进监测站”站点范围最小(均为3 km),且逐年分析显示,各个站点PM_(2.5)监测的代表范围呈上升趋势。     

Assessing temporal representativeness of water quality monitoring data

The effectiveness of different monitoring methods in detecting temporal changes in water quality depends on the achievable sampling intervals, and how these relate to the extent of temporal variation. However, water quality sampling frequencies are rarely adjusted to the actual variation of the monitoring area. Manual sampling, for example, is often limited by the level of funding and not by the optimal timing to take samples. Restrictions in monitoring methods therefore often determine their ability to estimate the true mean and variance values for a certain time period or season. Consequently, we estimated how different sampling intervals determine the mean and standard deviation in a specific monitoring area by using high frequency data from in situ automated monitoring stations. Raw fluorescence measurements of chlorophyll a for three automated monitoring stations were calibrated by using phycocyanin fluorescence measurements and chlorophyll a analyzed from manual water samples in a laboratory. A moving block bootstrap simulation was then used to estimate the standard errors of the mean and standard deviations for different sample sizes. Our results showed that in a temperate, meso-eutrophic lake, relatively high errors in seasonal statistics can be expected from monthly sampling. Moreover, weekly sampling yielded relatively small accuracy benefits compared to a fortnightly sampling. The presented method for temporal representation analysis can be used as a tool in sampling design by adjusting the sampling interval to suit the actual temporal variation in the monitoring area, in addition to being used for estimating the usefulness of previously collected data.     

Network design factors for assessing temporal variability in ground-water quality

Benchmark major ions and nutrients data were collected biweekly for about two years at 12 wells at two sites in a shallow sand and gravel aquifer in west-central Illinois. The purpose of the study was to explore the time series properties of ground-water quality data collected at a relatively high sampling frequency. A secondary purpose was to determine the relative magnitudes of natural and sampling-related sources of variance in ground-water quality time series. The absence of this kind of information has severely hindered the design of ground-water sampling programs in the past.An autocorrelation analysis showed that the median sampling frequency for which the predicted ratio of effective independent sample size to total sample size was 0.5 (50% sampling redundancy) ranged from 6 to 14 samples per year. For a predicted ratio of effective independent sample size to total sample size of 0.9 (10% sampling redundancy) the sampling frequency ranged from 3 to 6 samples per year. This suggests that, for the wells sampled, sampling frequencies much higher than monthly can result in considerable loss of information, and may not be cost effective. Care was taken in the design of the field and laboratory sampling protocol to minimize the effects of measurement error. The data analysis confirmed that this goal was accomplished. In most cases considerably less than five percent of the total variability could be attributed to sampling and analytical error. Because of the relatively short duration of the study (42 biweekly sampling occasions at most wells) it was not possible to identify the magnitude of seasonal variations reliably.     

Causes of temporal variability of lead in domestic plumbing systems

Sources of lead in drinking water are primarily lead pipe, lead/tin solder, and brass fixture materials.Lead levels in the water depend upon many solubility factors, such as pH, concentrations of substances such as inorganic carbonate, orthophosphate, chlorine, and silicate, the temperature, the nature of the pipe surface, etc. Physical factors, time, and chemical mass transfer are significant in governing lead levels in nonequilibrium systems. The diameter and length of lead pipe is extremely important, as well as the age and chemical history of the solder and brass fixtures. Analytical variability is not particularly significant relative to between-site and within-site variability. Knowledge of temporal variability at each site is necessary to define a statistically valid monitoring program. An analysis of published data covering repetitive measurements at a given site show that the variability of lead concentration at each site tends to be characterized by the frequent occurrence of spikes. Variability expressed as approximate relative standard deviations tends to be of about 50 to 75% in untreated water, regardless of the mean lead concentration. The distributions are frequently nonnormal for small numbers of samples. Monitoring programs must incorporate controls for the causes of the within-site and between-site variability into their sampling design. The determination of necessary sampling frequency, sample number, and sample volume must be made with consideration of the system variability, or the results will be unrepresentative and irreproducible.     

High temporal resolution monitoring of inorganic nitrogen load in drainage waters

Nitrate (NO3-), ammonium (NH4+) and pH were monitored with a novel flow cell equipped with ion-selective electrodes (ISEs) in a drainage pipe during one year. The high temporal resolution of the measurements (six measurements per hour) allowed the detection of diurnal oscillations in pH, NO3- and NH4+ concentrations, the relation of variations in concentrations to discharge rates changing during rain events, understanding of the processes resulting in such variations and tracing of unpredictable manure spills. Annual loads estimated from random samples collected every second day tended to underestimate the "true" loads calculated from quasi-continuous electrode measurements by 550% for NH4+ and 22% for NO3-.     

Groundwater monitoring plans at small-scale sites--an innovative spatial and temporal methodology

An innovative methodology for improving existing groundwater monitoring plans at small-scale sites is presented. The methodology consists of three stand-alone methods: a spatial redundancy reduction method, a well-siting method for adding new sampling locations, and a sampling frequency determination method. The spatial redundancy reduction method eliminates redundant wells through an optimization process that minimizes the errors in plume delineation and the average plume concentration estimation. The well-siting method locates possible new sampling points for an inadequately delineated plume via regression analysis of plume centerline concentrations and estimation of plume dispersivity values. The sampling frequency determination method recommends the future frequency of sampling for each sampling location based on the direction, magnitude, and uncertainty of the concentration trend derived from representative historical concentration data. Although the methodology is designed for small-scale sites, it can be easily adopted for large-scale site applications. The proposed methodology is applied to a small petroleum hydrocarbon-contaminated site with a network of 12 monitoring wells to demonstrate its effectiveness and validity.     

广西臭氧时空分布特征及污染天气类型研究

基于2015—2017年广西14个城市环境空气质量日监测数据和相关气象资料,分析了广西O_3时空分布特征,利用主观分型方法归纳了广西O_3污染的典型天气类型。结果表明,广西2015—2017年O_3累积超标天数分别为74,41和89 d,年均值分别为122,120和128μg/m~3,日变化呈现单峰型分布特征,O_3超标占比最高的时段均为4—5月和8—10月; 2017年O_3高值区面积明显增大,呈现连片式的特征;台风外围型(53. 4%)、副高控制型(26. 5%)和冷高压脊变性控制型(16. 7%)是造成广西O_3污染的典型天气类型。广西O_3污染受气象条件影响较大,天气形势变化导致的污染规律较明显。     

Normalization procedures for sediment contaminants in spatial and temporal trend monitoring

Rational pollution, or the effectiveness of natural attenuation assessments based upon estimating the degree of contamination, critically depends on the basis of a sound normalization to take into account heterogeneous sedimentary environments. By normalizing the measured contaminant concentration patterns for the sediment characteristics, the inherent variability can be reduced and so allow a more meaningful assessment of both the spatial distributions and the temporal trends. A brief overview and guidance in the methodology available for choosing an appropriate site-specific normalization approach is presented. This is followed by general recommendations with respect to the choice of normalizer and the necessary geochemical and statistical quality assurance methods, with support from the results of recent international intercomparison exercises within the QUASH (Quality Assurance of Sample Handling) programme, as well as discussions within the International Commission on the Exploration of the Sea (ICES) working groups. The most important of these recommendations is the use of a two-tiered normalization approach including wet sieving (<63 microm), followed by an additional geochemical co-factor normalization.     

Spatial and temporal variability in the relationship between cyanobacterial biomass and microcystins

The increasing incidence of toxic cyanobacterial blooms, together with the difficulties to reliably predict cyanobacterial toxin (e.g. microcystins) concentration, has created the need to assess the predictive ability and variability of the cyanobacterial biomass–microcystin relationship, which is currently used to assess the risk to human and ecosystems health. To achieve this aim, we assessed the relationship between cyanobacterial biomass and microcystin concentration on a spatiotemporal scale by quantifying the concentration of cyanobacterial biomass and microcystin in eight lakes over 9 months. On both a temporal and spatial scale, the variability of microcystin concentration exceeded that of cyanobacterial biomass by up to four times. The relationship between cyanobacterial biomass and microcystin was weak and site specific. The variability of cyanobacterial biomass only explained 25 % of the variability in total microcystin concentration and 7 % of the variability of cellular microcystin concentration. Although a significant correlation does not always imply real cause, the results of multiple linear regression analysis suggest that the variability of cyanobacterial biomass and cellular microcystin concentration is influenced by salinity and total phosphorus, respectively. The weak cyanobacterial biomass–microcystin relationship, coupled with the fact that microcystin was present in concentrations exceeding the WHO drinking water guidelines (1 μg?L^?1) in most of the collected samples, emphasizes the high risk of error connected to the traditional indirect microcystin risk assessment method.     

Spatial and temporal distribution of gaseous elemental mercury in Chongqing, China

In order to investigate the spatial and temporal variability of atmospheric mercury (Hg) in Chongqing, China, gaseous elemental mercury (GEM) was measured from August 2006 to September 2007, using Lumex multifunctional mercury analyzer RA-915(+) (Lumex Ltd., Russia). The mean GEM concentration was 6.74 +/- 0.37 ng m(-3) in Chongqing, much higher than the accepted global background values (1.5-2 ng m(-3)). The GEM concentrations were different in different function areas. GEM in transport, industrial and commercial areas were 7.07 +/- 1.04, 7.05 +/- 0.96 and 6.71 +/- 1.10 ng m(-3), respectively, while GEM was 6.14 +/- 1.30 and 4.32 +/- 1.04 in the educational/recreational and nature conservation areas, suggesting that Hg emissions from mobile vehicles and industrial sources (specially coal combustion) were the most important contributors to atmospheric Hg in Chongqing. Mean Hg concentrations also had monthly variations with highest in November (8.24 +/- 0.50 ng m(-3)) and lowest values in August (5.36 +/- 0.70 ng m(-3)). Additionally, the diurnal variation of GEM concentrations was dependent on the local/regional atmospheric conditions. At Jinyun Mountain site (natural conservation area), hourly GEM concentrations had much higher values in daytime than at night. At Power Plant site, however, the hourly GEM concentrations were lower in daytime than at night. GEM concentrations in the air were correlated significantly with meteorological parameters except for barometric pressure.     

Spatial and temporal occurrence of N-nitrosamines in seven drinking water supply systems

The spatiotemporal presence of eight N-nitrosamines in the water of seven supply systems in Quebec considered to be susceptible to these emerging disinfection by-products was evaluated. This is the first study on the presence of N-nitrosamines in drinking water utilities in Quebec. Seven sampling campaigns were carried out at several sampling points in each of the systems over a period of 1 year. The results show that N-nitrosamines, primarily N-nitrosodimethylamine (NDMA), were not commonly detected in the water of the facilities under study (10 % of samples). The concentrations measured were lower than those reported in recent North American studies. None of the 195 samples taken exceeded the Ontario standard of 9 ng/L for NDMA (maximum value observed of 3.3 ng/L). N-nitrosomethylethylamine and N-nitrosopiperidine were detected once, with concentrations of 3.7 and 6.0 ng/L, respectively. Chloramination was identified as being the main risk factor regarding the presence of N-nitrosamines, but water quality and some operating parameters, in particular disinfectant residual, also seem to be related to their presence. NDMA concentrations at the end of the distribution systems were generally higher than water leaving the plant. No seasonal trends were observed for the formation of N-nitrosamines in the investigated supply systems. Finally, an association between the presence of N-nitrosamines and the levels of trihalomethanes and haloacetic acids was observed in some facilities.