Spatial and Seasonal Response of Municipal Water Use to Weather across the Contiguous U.S.

Weather variability has the potential to influence municipal water use, particularly in dry regions such as the western United States (U.S.). Outdoor water use can account for more than half of annual household water use and may be particularly responsive to weather, but little is known about how the expected magnitude of these responses varies across the U.S. This nationwide study identified the response of municipal water use to monthly weather (i.e., temperature, precipitation, evapotranspiration [ET]) using monthly water deliveries for 229 cities in the contiguous U.S. Using city‐specific multiple regression and region‐specific models with city fixed effects, we investigated what portion of the variability in municipal water use was explained by weather across cities, and also estimated responses to weather across seasons and climate regions. Our findings indicated municipal water use was generally well‐explained by weather, with median adjusted R² ranging from 63% to 95% across climate regions. Weather was more predictive of water use in dry climates compared to wet, and temperature had more explanatory power than precipitation or ET. In response to a 1°C increase in monthly maximum temperature, municipal water use was shown to increase by 3.2% and 3.9% in dry cities in winter and summer, respectively, with smaller changes in wet cities. Quantifying these responses allows urban water managers to plan for weather‐driven variability in water use.     

Assessing Spatial, Temporal, and Analytical Variation of Groundwater Chemistry in a Large Nuclear Complex, USA

Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a ‘pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s ², which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

基于时序NDVI数据的鄱阳湖流域常绿覆被季节性变化特征

选择位于红壤丘陵区的鄱阳湖流域作为研究对象，利用1 km×1 km分辨率的时序SPOT4 VEGETATION数据，对流域内典型土地覆被--常绿覆被的绿度值、峰值、谷值、年均NDVI(NDVI-I)和NDVI年内极差(NDVI MM)等特征值进行了提取。在此基础上，探讨了不同常绿覆被类型的NDVI指数年内季节变化规律。结果表明：时序NDVI指数基本上能够较好地刻画不同常绿覆被类型之间的差异性，植被指数NDVI特征值随覆被的类型及其生长状态有规律地变化，即NDVI年均值和最小值基本上按“常绿阔叶林＞常绿针阔叶混交林＞常绿针叶林＞常绿针叶-落叶混交林”的顺序变化；典型常绿阔叶林的NDVI指数年内变化曲线基本上没有大的起伏波动；常绿针叶林以及常绿针阔叶混交林占主导地位的常绿混交林NDVI指数年内变化比较和缓，但常在8月和11月有所波动；以常绿针叶林为主、但有较多落〖JP2〗叶林混杂其中的常绿混交林，其NDVI指数年内变化曲线基本上呈和缓的单峰型波动。     

Health and environmental assessment of mercury exposure in a gold mining community in Western Mindanao, Philippines

The small-scale gold mining activities using mercury began in the late 1980s in Sibutad, Western Mindanao. It is located very near the Murcielagos Bay with tailing ponds directly discharging into bodies of water. This cross-sectional study aimed to determine the health and environmental effects of gold mining activities on the community. Residents were randomly selected and classified into two groups, namely, the directly exposed and indirectly exposed populations using a set inclusion criteria. Complete medical and laboratory examinations were performed. Environmental and biologic samples were collected for total mercury and methylmercury determinations. The results showed that the directly exposed group had significantly higher mean blood total mercury and methylmercury levels in comparison with the indirectly exposed population. Although there were no significant differences between hair total mercury and methylmercury levels, there was a trend for higher levels of these biomarkers among the directly exposed residents as compared with the unexposed group. The absence of statistically significant differences may be attributable to the small sample size. Ambient air quality monitoring for mercury exceeded the allowable levels. However, levels of mercury in drinking water and sediments were within allowable limits. Frequency of gastrointestinal complaints was significantly associated with elevated hair methylmercury levels (p=0.02). Also, there appears to be a trend towards higher blood total mercury levels and frequency of gastrointestinal complaints (p=0.09). An interesting finding in this study was the increasing incidence of elevated diastolic blood pressure with elevated hair total mercury levels (p=0.07). Mercury storage at home is a risk factor.     

北京南部城区PM2.5中碳质组分特征

为了解《大气污染防治行动计划》实施后北京市大气PM_2.5中碳质组分特征,于2017年12月至2018年12月在北京污染较重的南部城区进行了PM_2.5连续采样,对其中的有机碳（OC）和元素碳（EC）进行了全面研究.结果表明,北京大气PM_2.5、OC和EC浓度变化范围分别为4.2~366.3、0.9~74.5和0.0~5.5 μg ·m^-3,平均浓度分别为（77.1±52.1）、（11.2±7.8）和（1.2±0.8）μg ·m^-3,碳质组分（OC和EC）整体占PM_2.5的16.1%.OC质量浓度季节特征表现为：冬季[（13.8±8.7）μg ·m^-3] > 春季[（12.7±9.6）μg ·m^-3] > 秋季[（11.8±6.2）μg ·m^-3] > 夏季[（6.5±2.1）μg ·m^-3],EC四季质量浓度水平均较低,范围为0.8~1.5 μg ·m^-3.二次有机碳（SOC）年均质量浓度为（5.4±5.8）μg ·m^-3,四季贡献比例范围为45.7%~52.3%,年均贡献为48.2%,凸显了二次形成的重要贡献.随污染加重,尽管OC和EC贡献比例均降低,但浓度水平却成倍升高,OC和EC浓度在严重污染天分别是空气质量为优天的6.3和3.2倍.与非供暖时段相比,供暖时段PM_2.5、OC和SOC浓度分别增加了14.4%、47.9%和72.1%,体现了OC对供暖季PM_2.5污染的重要贡献.PSCF分析表明,位于北京西南的山西省和河南省部分区域是PM_2.5和OC的主要潜在源区,且PM_2.5潜在源区更为集中;EC的PSCF高值（>0.7）区域较少,主要位于北京南部,如山东省和河南省部分地区,且北京市及周边地区贡献明显.     

保定市大气污染特征和潜在输送源分析

保定市是京津冀地区重要城市之一.为了解保定市大气污染物质量浓度特征和潜在输送源,对保定市国控点2017年1月1日-12月31日PM₁₀、PM_2.5、SO₂、NO₂、O₃、CO等常规大气污染物数据进行分析,并利用TrajStat后向轨迹模型进行区域传输研究.结果表明:①ρ（PM₁₀）、ρ（PM_2.5）、ρ（SO₂）、ρ（NO₂）分别为（138±96）（84±66）（29±23）和（50±24）μg/m3,与2016年相比分别下降5.9%、9.1%、25.5%和13.1%;ρ（CO）较2016年下降了14.0%;ρ（O₃）较2016年增长了25.2%.ρ（PM₁₀）、ρ（PM_2.5）、ρ（NO₂）和ρ（O₃）分别超过GB 3095-2012《环境空气质量标准》二级标准限值的0.97、1.40、0.25和0.34倍,ρ（SO₂）和ρ（CO）未超标.②除ρ（O₃）外,其他污染物质量浓度均呈冬季最高、夏季最低的季节性特征,其中,冬季PM_2.5污染最为严重,春季PM_2.5~10（粗颗粒物）污染严重.③空气质量模型源解析结果显示,保定市ρ（PM_2.5）约60.0%~70.0%来自本地污染源排放.后向轨迹结果表明,在外来区域传输影响中,保定市主要受到西北方向气团（占比为21.7%~60.0%）远距离传输和正南方向气团（占比为34.8%~50.5%）近距离传输的影响.④PSCF（潜在源贡献因子分析法）和CWT（浓度权重轨迹分析法）分析表明,除保定市及周边区县本地污染贡献外,位于太行山东麓沿线西南传输通道的邯郸市、邢台市、石家庄市是影响保定市PM_2.5的主要潜在源区.研究显示,PM_2.5为保定市大气中的主要污染物,并呈冬季高、夏季低的变化特征,其主要来自西北远距离输送和南部近距离传输.      

我国能源资源税改革对城乡居民的收入分配效应——基于CGE模型的分析

通过构建包含7个城镇居民组和5个农村居民组的多收入阶层CGE模型,并将劳动力要素分为农业劳动力、技术工人和生产工人3类,在低税率、实际税率和高税率三种情景下模拟了煤炭、石油和天然气资源税改革单独实施和同时实施对城镇和农村居民的收入分配效应,并对税收补偿措施的效果进行分析.结果表明：单独实施煤炭资源税或天然气资源税改革对城镇居民组收入差距有正向的缩小作用,有利于收入分配公平,但对农村居民组收入差距起到负向的扩大作用,不利于收入分配公平;单独实施石油资源税改革则同时缩小了城镇和居民组的收入差距,有利于收入分配公平;同时实施三类能源资源税改革的收入分配效应与税率组合方式有关;将能源资源税收入以补贴的方式返还给农村居民可以缩小其收入差距,有利于收入分配公平.     

未来气候变化对武夷山自然保护区毛竹异戊二烯排放速率的影响

采用全球气候模式Nor ESM1-M产生的RCP2.6、RCP4.5、RCP6.0和RCP8.5气候变化情景数据和植物异戊二烯排放计算模型,模拟分析了未来气候变化对武夷山自然保护区毛竹(Phyllostachys pubescens)异戊二烯排放速率的影响.结果显示,气候变化下武夷山自然保护区气温上升,年降水量和辐射强度波动较大,呈增加或下降趋势.毛竹异戊二烯平均日排放速率在未来气候变化情景下比基准情景下高约30μg·g~(-1)·d~(-1),在RCP8.5情景下比基准情景下高约48μg·g~(-1)·d~(-1);毛竹异戊二烯日排放速率在未来气候变化情景与基准情景下的差异在1~90 d和301~365 d较小,在91~300 d差异较大;相比基准情景,未来气候变化情景下毛竹异戊二烯日排放速率在1~190 d(平均增加15%以上)和271~365 d(平均增加20%)增幅较大,在191~270 d增幅较小,在RCP8.5情景下增幅最大(平均增加17%).另外,毛竹异戊二烯年排放速率在未来气候变化情景下比基准情景下约高10000μg·g~(-1)·a~(-1)以上,在RCP8.5情景下比基准情景下约高13%.研究表明,未来气候变化将使毛竹异戊二烯排放速率增加.     

Effects of Na+ on Cu/SAPO-34 for ammonia selective catalytic reduction

Copper-exchanged chabazite (Cu/CHA) catalysts have been found to be affected by alkali metal and alkaline earth ions. However, the effects of Na⁺ ions on Cu/SAPO-34 for ammonia selective catalytic reduction (NH₃-SCR) are still unclear. In order to investigate the mechanism, five samples with various Na contents were synthesized and characterized. It was observed that the introduced Na⁺ ion-exchanges with H⁺ and Cu^2 + of Cu/SAPO-34. The exchange of H⁺ is easier than that of isolated Cu^2 +. The exchanged Cu^2 + ions aggregate and form “CuAl₂O₄-like” species. The NH₃-SCR activity of Cu/SAPO-34 decreases with increasing Na content, and the loss of isolated Cu^2 + and acid sites is responsible for the activity loss.