The impact of heat waves on daily mortality in Belgrade (Serbia) during summer

In this paper, we are dealing with two extreme events in temperature and population – heat wave and mortality. Our aim is to assess the relation between high temperatures and daily mortality counts during the summer months in the period 2000–2010 in Belgrade (Serbia). In order to establish this connection, we used Poisson regression and two different measures of heat wave: Warm Spell Duration Index (WSDI) and apparent temperature (T _app). As mean daily temperature increases over 90th, 95th and 99th percentiles, average number of deaths increases for 15.3% (p?<?.01), 22.4% (p?<?.05) and 32.0% (insignificant for p?<?.1). We tested three different thresholds (90th, 95th, 99th) for WSDI and T _app in order to separate the hottest heat-wave episodes. On average, mortality is higher than expected for 13.4%, 16.7% and 28.3% (90th, 95th and 99th percentiles for WSDI) and 16.1%, 17.3% and 32.5% (90th, 95th and 99th percentiles for T _app). Estimated mortality excess with heat-wave indices is in accordance with regression output, meaning that WSDI and T _app are good tools for heat-wave identification. During the most severe episode in July 2007, absolute temperature maximum (43.6°C) and daily maximum mortality counts (94 dead) were recorded in the same day (24 July 2007).     

Temporal variability of visibility and its parameterizations in Ningbo, China

Simultaneous and continuous measurements of visibility, meteorological parameters and air pollutants were carried out at a suburban site in Ningbo from June 1, 2013 to May 31,2015. The characteristics of visibility and their relationships with air pollutants and meteorological factors were investigated using multiple statistical methods. Daily visibility ranged from 0.6 to 34.1 km, with a mean value of 11.8 km. During the 2-year experiment,43.4% of daily visibility was found to be less than 10.0 km and only 9.2% was greater than 20.0 km. Visibility was lower in winter with a frequency of 53.4% in the range of 0.0–5.0 km.Annual visibility had an obvious diurnal variation, with the lowest and highest visibility being 7.5 km at approximately 06:00 local time and 15.6 km at approximately 14:00 local time, respectively. Multiple correspondence analysis(MCA) indicated that the different ranges of visibility were significantly affected by different levels of pollutants and meteorological conditions. Based on the analyses, visibility was found to be an exponential function of PM_(2.5) concentrations within a certain range of relative humidity. Thus, nonlinear models combining multiple linear regressions with exponential regression were subsequently developed using the data collected from June 2014 to May 2015, and the data from June 2013 to May 2014 was used to evaluate the performance of the model. It was demonstrated that the derived models can quantitatively describe the relationships between visibility, air quality and meteorological parameters in Ningbo.     

农业灌溉用水经济价值及其影响因素——基于剩余价值法和陕西关中地区农户调研数据

灌溉水经济价值是农业用水定价的重要基础。根据陕西关中地区农户调研数据,采用剩余价值法计算得到小麦、玉米和苹果灌溉水经济价值均值分别为0.66 元/m³、0.84 元/m³、3.17 元/m³。以该结果作为水价调整上限与现状水价比较,小麦和玉米的水价上调空间介于0.24~0.71元/m³,苹果的水价调整空间介于2.72~3.04 元/m³之间。使用分位数模型对影响因素的研究发现：（1）仅在灌溉水经济价值较低时,农户和家庭特征以及土地分散程度有显著影响;（2）在灌溉水经济价值较高时,增强农户节水意识以及加入用水者协会将能有效提升灌溉用水经济价值;（3）粮食转为经济作物、进行农业水价综合改革在所有分位点上均呈显著正向影响。本文可为研究区域农业用水分类定价及改善用水效率提供参考。     

Statistical regression modeling for energy consumption in wastewater treatment

Wastewater treatment is one of critical issues faced by water utilities, and receives more and more attentions recently. The energy consumption modeling in biochemical wastewater treatment was investigated in the study via a general and robust approach based on Bayesian semi-parametric quantile regression. The dataset was derived from a municipal wastewater treatment plant, where the energy consumption of unit chemical oxygen demand (COD) reduction was the response variable of interest. Via the proposed approach, the comprehensive regression pictures of the energy consumption and truly influencing factors, i.e., the regression relationships at lower, median and higher energy consumption levels were characterized respectively. Meanwhile, the proposals for energy saving in different cases were also facilitated specifically. First, the lower level of energy consumption was closely associated with the temperature of influent wastewater, and the chroma-rich wastewater also showed helpful in the execution of energy saving. Second, at median energy consumption level, the COD-rich wastewater played a determinative role in the reduction of energy consumption, while the higher quality of treated water led to slightly energy intensive. Third, the higher level of energy consumption was most likely to be attributed to the relatively high temperature of wastewater and total nitrogen (TN)-rich wastewater, and both of the factors were preferably to be avoided to alleviate the burden of energy consumption. The study provided an efficient approach to controlling the energy consumption of wastewater treatment in the perspective of statistical regression modeling, and offered valuable suggestions for the future energy saving.     

北京市PM2.5时空分布特征及其与PM10关系的时空变异特征

PM_(2.5)时空分布特征及其与其它污染物的相关关系是PM_(2.5)时空统计分析的主要研究内容.然而,现有的方法直接从监测站点的角度对时空分布特征进行分析,难以有效地揭示PM_(2.5)浓度的聚集分布特征;同时,常用的地理加权回归在对PM_(2.5)与其它污染物间关系进行建模的过程中,缺乏同时考虑时间异质性与空间异质性,从而不能准确地描述依赖关系的时空变异特征.为此,首先借助于空间聚类分析技术,对北京市2014年PM_(2.5)浓度的聚集结构进行探测,在此基础上,通过聚集结构来分析PM_(2.5)季节性时空分布特征.然后,利用地理时空加权回归对北京市PM_(2.5)与PM_(10)季节平均浓度间关系进行建模,依据回归结果分析PM_(2.5)-PM_(10)间关系的时空变异特征.实验结果表明,春夏季节PM_(2.5)污染程度及空间变异程度均低于秋冬季节,各季节PM_(2.5)浓度均表现为北部浓度低、南部浓度高的空间分布特征;地理时空加权回归具有更好的拟合效果,由回归系数进一步可发现,春夏季PM_(2.5)-PM_(10)相关性低于秋冬季PM_(2.5)-PM_(10)相关性;各季节均表现为西北部PM_(2.5)-PM_(10)的相关性高于东南部PM_(2.5)-PM_(10)的相关性.     

京津冀地区高分辨率PM2.5浓度时空变化模拟与分析

为了全覆盖、高分辨率和高精度识别京津冀地区大气PM_2.5质量浓度时空变化,选取多角度大气校正算法遥感反演的1km AOD为主要预测因子,多种气象要素和土地利用要素为辅助预测因子,构建了混合效应模型+地理加权回归模型的两阶段统计模型,并针对京津冀地区PM_2.5污染较严重的特点,模型中引入了AOD²等独特预测因子.通过上述两阶段模型定量预测了研究区2017年1 km²空间分辨率的每日PM_2.5质量浓度.结果表明,模型交叉验证的决定系数R²为0.94,斜率为0.95,均方根预测误差为13.14 μg·m^-3,在前人基础上预测精度进一步提升,可用于PM_2.5浓度时空变化预测与分析.2017年,京津冀地区PM_2.5浓度年均值为44.96 μg·m^-3,年均值范围在0~89.89 μg·m^-3之间.PM_2.5浓度时空变化差异性明显,整体上呈现"平原西南部浓度高、平原东北部浓度中等和山区高原浓度低"的空间分布格局以及"冬季浓度高、夏季浓度低和春秋过渡"的季节变化特点.模型预测结果的高时空分辨率可以支持流行病学研究在较小区域的暴露评估和识别小尺度污染源的时空变化,分析发现在大气污染防治行动计划实施以来,污染较严重的冀中南山麓平原区可能出现了重要污染源的空间变化.模型预测与分析结果可以为京津冀大气污染防治提供科学支撑.     

中国碳排放及影响因素的市域尺度分析

评估区域碳排放及其与社会经济状况的关系对于制定碳减排措施至关重要.以中国339个地级及以上城市(不含新疆部分城市和港澳台地区)为研究对象,探究了非化石能源占比、土地开发度、常住人口城镇化率、第二产业占比、人均GDP和人均建设用地面积对人均CO₂排放量的影响.通过构建模拟人均CO₂排放量的贝叶斯信念网络,识别各因素对人均CO₂排放量的全局影响;采用多尺度地理加权回归模型,分析各因素对人均CO₂排放量的局部影响.结果表明：(1)2020年,中国地级及以上城市人均CO₂排放量呈现出由南向北递增,东部沿海向内陆递减的格局.(2)从全局来看,人均CO₂排放量对各因素的敏感性从高到低依次为：人均建设用地面积>人均GDP>常住人口城镇化率>土地开发度>第二产业占比>非化石能源占比.(3)从局部来看,各因素与人均CO₂排放量的空间关系方向与全局关系一致,关系强度上存在空间异质性.(4)清洁能源、脱碳技术、土地节约集约利用...     

SPATIAL AND TEMPORAL VARIABILITY IN SUSPENDED SEDIMENT YIELDS OF NORTH ISLAND BASINS,NEW ZEALAND1

ABSTRACT: Specific annual suspended sediment yields and their standard deviations are presented for 47 basins of North Island, New Zealand. Most of the variance in yields is explained by catchment mean rainfall. Rivers with similar flow range have similar suspended sediment concentration ratings, independent of differing watershed lithology and regolith, except for six basins having an abundance of soft fine sediments. Prediction equations for yield and its standard deviation are derived for four essentially arbitrary regions. AU feature rainfall as the independent variable. Differences between regions may owe to variations in intensity, frequency, and duration patterns of storms and, in one area, to bed material size as well. The temporal distribution of annual yields from a basin m be modeled by a two-parameter lognormal function: the prediction equations above may be used to evaluate this function at a site for which suspended sediment data are unavailable.     

Characteristics of roadside air pollution in Korean metropolitan city (Daegu) over last 5 to 6 years: temporal variations, standard exceedances, and dependence on meteorological conditions

The present study performed a roadside data analysis to provide baseline data for exploring associations between environmental exposure to four gaseous pollutants and health effects on residents living near roadways. The yearly roadside concentrations of CO and SO₂ showed a well-defined decreasing trend, whereas those of NO₂ and O₃ exhibited the reverse trend. In most cases, the diurnal trends of the roadside concentrations were well-defined for all seasons, plus the daytime concentrations were higher than the nighttime concentrations. In contrast to the other target pollutants, the daytime O₃ concentrations observed at the roadside sites were lower than those observed at the residential site, likely due to high-levels of fresh NO from traffic emissions that rapidly react with O₃, thereby reducing the O₃ roadside level. The Sunday roadside concentrations of CO, NO₂, and SO₂ were similar to or somewhat lower than the weekday concentrations. Conversely, for O_3, the Sunday roadside concentrations were similar to or somewhat higher than the weekday concentrations. The higher O₃ concentrations on Sunday may be due to the reduced titration from a decrease in NO_x emissions under VOC-limited conditions (low VOC/NO_x conditions). The monthly averages of O₃ concentrations exhibited the reverse seasonal variation to the other target compounds, with peak O₃ concentrations between April and June, and the second peak between August and October. It is also suggested that for O₃, the 8-h standard is more stringent than the 1-h standard, while for NO₂ and SO₂, the 1-h standard is more stringent than the 24-h standard. The multiple regression equations obtained from the relationship between the concentrations and five meteorological parameters indicated that the number and type of meteorological variables in the equations varied according to the pollutant, monitoring station, or season.