神经网络模型在北海市空气日报污染指数计算中的应用

根据空气质量日报的实际需要,引入了Excel软件神经网络技术建立空气质量日报污染指数计算模型,采用LM算法提高了计算精度,并将模型应用于北海市空气日报。结果表明:此法较之实际公式计算法更加快捷方便,并且计算结果相当吻合。     

2003~2012年西宁市空气质量污染现状分析

文章利用2003~2012年西宁市环境空气质量日报数据,参照《环境空气质量标准》(GB3095-1996),用统计分析法分析西宁市近10年环境空气质量污染现状。结果表明:2003~2012年西宁市环境空气的首要污染物主要为可吸入颗粒物,空气污染以轻微污染为主,采暖期污染重于非采暖期;污染物可吸入颗粒物主要来自于汽车尾气、地表扬尘和建筑灰尘等。     

成渝经济区重点城市空气质量模糊综合评价

成渝经济区是中国新的经济增长极,随着经济的快速发展,城市空气污染日趋严重。通过收集成渝经济区7个重点城市2006～2010年空气污染指数数据,以平均空气污染指数、优良率、污染率和严重污染率为评价指标对城市空气质量进行模糊综合评价。结果表明城市空气质量优劣依次为南充、自贡、德阳、绵阳、重庆、泸州和成都。模糊综合评价应用于城市空气质量比较是合理的。     

空气质量指数计算方法在Excel中VBA的实现

空气质量指数计算方法是目前全国各城市空气质量重要信息发布的基础,广泛应用于城市空气自动监测的空气质量评价。依据《HJ 633-2012环境空气质量指数(AQI)技术规定(试行)》发布环境空气质量指数(AQI)计算方法,利用VBA在Excel下编写宏程序,可以轻松实现空气质量日报信息的发布,使用户从手工重复性的填充和插入函数操作方式中彻底解放出来。     

空气质量指数计算方法在Excel中VBA的实现简

空气质量指数计算方法是目前全国各城市空气质量重要信息发布的基础,广泛应用于城市空气自动监测的空气质量评价。依据《HJ 633-2012环境空气质量指数（AQI）技术规定（试行）》发布环境空气质量指数（AQI）计算方法,利用VBA在Excel下编写宏程序,可以轻松实现空气质量日报信息的发布,使用户从手工重复性的填充和插入函数操作方式中彻底解放出来。     

汽车节能环保评价与能耗污染指数探讨

本文分析了汽车能耗和污染涉及的多个因子,根据它们对大气环境和人体健康的损害程度,赋予其不同权重系数,论证确定各自基准值后,提出了量化的可供统一比较的汽车能耗污染指数,并据此进行了汽车节能环保星级评定.收集部分中国新车型数据进行了实例分析.     

保障空气质量为奥运保驾护航——北京市机动车排放污染控制谈

随着城市的发展、人民生活水平的提高,机动车保有量也在快速增加,到2007年5月底北京市机动车保有量已经超过了300万辆,居全国第一位.     

重庆市多年空气污染指数分析及大气污染控制对策

本文分析了重庆市主城区2001～2003年的API指数,得出API分布及变化规律,结果显示大气污染变化规律受季节及气象因素影响,在此基础上,提出了重庆市主城区大气污染控制对策。     

上海市空气污染指数API的时空分布特征

空气污染指数(API)是一种综合反应和评价空气质量的指标,分级表征空气污染程度和空气质量状况.利用上海市环境监测中心收集的上海市2006 ～2011年各个区的API指数,对API指数进行时间序列分析和空间分析,包括近6年来API的年变化、季节变化以及比较上海市市区和郊区的API值.结果表明:上海市的空气污染状况在2006～2011年期间得到改善;夏季的空气质量最好,春季最差;空间上存在城郊差异,郊区API指数低于市区,金山区空气质量最好,普陀区和宝山区质量较差.     

广州市2002～2003年空气污染指数分析

根据广州市2002年1月1日至2003年12月31日的空气污染指数资料,对广州市每日的空气污染指数和可吸入颗粒物、NO2和SO2的污染指数、空气污染指数的月平均值和年平均值、空气质量级别、首要污染物等指标进行了统计分析.结果表明,广州市的空气污染状况与大尺度的天气现象如季风和降雨量等密切相关.     

Sedimentation rate of settleable particulate matter in Santiago city,Chile

Settleable particulate matter (SPM) is an atmospheric pollutant harmful to human health and the environment in high concentrations. Despite this fact, no up‐to‐date information on SPM levels exists for the capital of Chile, Santiago (7 million inhabitants). To address this knowledge gap, SPM sedimentation rates, including soluble and insoluble components, were measured at three different urban sites from July to November of 2016. We compare the measurements with ambient and meteorological information, as well as urban typology settings. Our results indicate SPM deposition rates between 2.5 and 3.9 g/(m²·30 days). Only one site exceeded the national limit of 4.5 g/(m²·30 days), but we found an increasing trend in all three sites. SPM and its insoluble sedimentation rates increased during warm and dry months and presented significant correlations with meteorological parameters. The highest sedimentation rates were measured at the location with the least permeable surfaces and the lowest green spaces, while the lowest sedimentation rates were found in the sites with abundant green spaces and permeable soil. No significant differences were detected in the soluble components. Our results suggest that SPM levels in Santiago are close to the national limit and may increase with climate change and urban expansion.     

Measuring neighbourhood air pollution: the case of Seattle's international district

Current US regulatory air quality monitoring networks measure ambient levels of pollutants and cannot capture the effects of mobile sources at the micro-scale. Despite the fact that overall air quality has been getting better, more vulnerable populations (children, the elderly, minorities and the poor) continue to suffer from traffic-related air pollution. As development intensifies in urban areas, more people are exposed to road-related air pollution. However, the only consideration given to air quality, if any, is based on ambient measures. This paper uses an inexpensive, portable Particle Soot Absorption Photometer (PSAP) to measure Black Carbon (BC) emissions, a surrogate for diesel fuels emissions, in Seattle's International District. With the aid of a GPS receiver, street-level BC data were geocoded in real space-time. It was found that pollution levels differed substantially across the study area. The results show the need for street-level air pollution monitoring, revisions in current land use and transportation policies, and air quality planning practice.     

浅析攀枝花市2015 ～ 2017年气象要素对空气污染的影响

利用2015～2017年攀枝花市污染因子和气象要素实测数据,运用GIS技术、相关分析以及统计分析等方法,分析攀枝花市空气污染因子和气象要素的时空特征及相关性。结果表明:攀枝花市首要污染物为PM₁₀,其次是NO₂;秋冬以PM₁₀和NO₂污染为主,春夏PM₁₀和O₃为主。不同季节,各气象要素对本地空气质量的影响程度及空间分布存在明显差异。弄弄坪一带PM_2.5和SO₂、CO浓度偏高,市中心炳草岗一带PM₁₀、O₃和NO₂浓度偏高。结合本地发展规划和实际情况,根据气象要素分析,为攀枝花市分区分季节的防污减排决策提供气象参考。     

Using Spatially Detailed Water‐Quality Data and Solute‐Transport Modeling to Support Total Maximum Daily Load Development1

Walton‐Day, Katherine, Robert L. Runkel, and Briant A. Kimball, 2012. Using Spatially Detailed Water‐Quality Data and Solute‐Transport Modeling to Support Total Maximum Daily Load Development. Journal of the American Water Resources Association (JAWRA) 48(5): 949‐969. DOI: 10.1111/j.1752‐1688.2012.00662.x Abstract: Spatially detailed mass‐loading studies and solute‐transport modeling using OTIS (One‐dimensional Transport with Inflow and Storage) demonstrate how natural attenuation and loading from distinct and diffuse sources control stream water quality and affect load reductions predicted in total maximum daily loads (TMDLs). Mass‐loading data collected during low‐flow from Cement Creek (a low‐pH, metal‐rich stream because of natural and mining sources, and subject to TMDL requirements) were used to calibrate OTIS and showed spatially variable effects of natural attenuation (instream reactions) and loading from diffuse (groundwater) and distinct sources. OTIS simulations of the possible effects of TMDL‐recommended remediation of mine sites showed less improvement to dissolved zinc load and concentration (14% decrease) than did the TMDL (53‐63% decrease). The TMDL (1) assumed conservative transport, (2) accounted for loads removed by remediation by subtracting them from total load at the stream mouth, and (3) did not include diffuse‐source loads. In OTIS, loads were reduced near their source; the resulting concentration was decreased by natural attenuation and increased by diffuse‐source loads during downstream transport. Thus, by not including natural attenuation and loading from diffuse sources, the TMDL overestimated remediation effects at low flow. Use of the techniques presented herein could improve TMDLs by incorporating these processes during TMDL development.     

GROUPWISE MODELING STUDY OF BACTERIALLY IMPAIRED WATERSHEDS IN TEXAS: CLUSTERING ANALYSIS1

ABSTRACT: Under the Clean Water Act (CWA) program, the Texas Commission on Environmental Quality (TCEQ) listed 110 stream segments in the year 2000 with pathogenic bacteria impairment. A study was conducted to evaluate the probable sources of pollution and characterize the watersheds associated with these impaired water bodies. The primary aim of the study was to group the water bodies into clusters having similar watershed characteristics and to examine the possibility of studying them as a group by choosing models for total maximum daily load (TMDL) development based on their characteristics. This approach will help to identify possible sources and determine appropriate models and hence reduce the number of required TMDL studies. This in turn will help in reducing the effort required to restore the health of the impaired water bodies in Texas. The main characteristics considered for the classification of water bodies were land use distribution within the watershed, density of stream network, average distance of land of a particular use to the closest stream, household population, density of on‐site sewage facilities (OSSFs), bacterial loading from different types of farm animals and wildlife, and average climatic conditions. The climatic data and observed instream fecal coliform bacteria concentrations were analyzed to evaluate seasonal variability of instream water quality. The grouping of water bodies was carried out using the multivariate statistical techniques of factor analysis/principal component analysis, cluster analysis, and discriminant analysis. The multivariate statistical analysis resulted in six clusters of water bodies. The main factors that differentiated the clusters were found to be bacterial contribution from farm animals and wildlife, density of OSSFs, density of households connected to public sewers, and land use distribution.