A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA

Land use regression (LUR) models have been used successfully for predicting local variation in traffic pollution, but few studies have explored this method for deriving fine particle exposure surfaces. The primary purpose of this method is to develop a LUR model for predicting fine particle or PM(2.5) mass over the five county metropolitan statistical area (MSA) of Los Angeles. PM(2.5) includes all particles with diameter less than or equal to 2.5 microns. In the Los Angeles MSA, 23 monitors of PM(2.5) were available in the year 2000. This study uses GIS to integrate data regarding land use, transportation and physical geography to derive a PM(2.5) dataset covering Los Angeles. Multiple linear regression was used to create the model for predicting the PM(2.5) surface. Our parsimonious model explained 69% of the variance in PM(2.5) with three predictors: (1) traffic density within 300 m, (2) industrial land area within 5000 m, and (3) government land area within 5000 m of the monitoring site. These results suggest the LUR method can refine exposure models for epidemiologic studies in a North American context.     

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

This paper examines the application of artificial neural network (ANN) and boosted regression tree (BRT) methods in air quality modelling. The methods were applied to developing air quality models for predicting roadside particle mass concentration (PM₁₀, PM_2.5) and particle number counts (PNC) based on air pollution, traffic and meteorological data from Marylebone Road in London. Elastic net, Lasso and principal components analysis were used as feature selection methods for the ANN models to reduce the number of predictor variables and improve their generalisation. The performance of the ANN with feature selection (ANN hybrid) and the BRT models was evaluated and compared using statistical performance metrics. The performance parameters include root mean square error (RMSE), fraction of prediction within a factor of two of the observation (FAC2), mean bias (MB), mean gross error (MGE), the coefficient of correlation (R) and coefficient of efficiency (CoE) values. The input variables selected by the elastic net produced the best performing ANN models. The ANN hybrid produced models performed only slightly better than the BRT models. The R values of the ANN elastic net and BRT models were 0.96 and 0.95 for PM₁₀, 0.96 and 0.96 for PM_2.5 and 0.89 and 0.87 for PNC, respectively. Their corresponding CoE values were 0.72 and 0.70 for PM₁₀, 0.74 and 0.76 for PM_2.5 and 0.81 and 0.71 for PNC respectively. About 80–99% of all the model predictions are within a factor of two of the observed particle concentrations. The BRT models offer more advantages regarding model interpretation and permit feature selection. Therefore, the study recommends the use of BRT over ANN where the model interpretation is a priority.     

Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area

Istanbul is the most populated city of Turkey with a population of around 10.58 M (2000) living on around 5,750 km². In 1980, the population was only 4.7 M and then it has been more than doubled in only two decades. The population has been increasing as a result of mass immigration. An urbanization process continues and it causes serious increases in urban areas while decreasing the amount of green areas. This rapid, uncontrolled, and illegal urbanization accompanied by insufficient infrastructure has caused degradation of forest and barren lands in the metropolitan area, especially through the last two decades. The watershed basins inside the metropolitan area and the transportation network have accelerated the land-cover changes, which have negative impacts on water quality of the basins. Monitoring urban growth and land cover change will enable better management of this complex urban area by the Greater Istanbul Metropolitan Municipality (GIMM). A temporal assessment of land-cover changes of Istanbul has been documented in this study. The study mainly focuses on the acquisition and analysis of Landsat TM and Landsat GeoCover LC satellite images reflecting the significant land-cover changes between the years of 1990 and 2005. Raster data were converted to vector data and used in Geographic Information Systems (GIS). A database was created for Istanbul metropolitan area to plan, manage, and utilize statistical attribute data covering population, water, forest, industry, and topographic position. Consequently an overlay analysis was carried out and land use/cover changes through years have been detected for the case study area. The capability of Landsat images in determining the alterations in the macro form of the city are also discussed.     

Determination of n-alkanes and polycyclic aromatic hydrocarbons in atmospheric particulate and vapour phases in Oviedo, Spain, by GC-MS

A GC-MS procedure for the determination of hydrocarbons in air samples from Oviedo, Spain, was developed. Air hydrocarbons were sampled with a high volume sampler equipped with a holder containing a glass fiber filter, to trap the particulate phase, and two polyurethane foams to capture hydrocarbons of the vapour phase. Compounds were extracted with CH2Cl2 by Soxhlet extraction and then fractionated using column chromatography with alumina silica. Analyses of the fractions were performed by GC-MS in the electron ionization mode. PAHs and n-alkanes were the compounds examined in this work. Samples collected in the vicinity of the Faculty of Chemistry (a semi-urban area) were analysed. The total concentration of PAHs in the air samples analysed ranged from 28 to 76 ng m(-3). The total concentration of n-alkanes and PAHs in the vapour phase exceeded the concentration in the particulate phase in the samples analysed.     

Forecasting Air Quality Parameters Using Hybrid Neural Network Modelling

Urban air pollution has emerged as an acute problem in recent years because of its detrimental effects on health and living conditions. The research presented here aims at attaining a better understanding of phenomena associated with atmospheric pollution, and in particular with aerosol particles. The specific goal was to develop a form of air quality modelling which can forecast urban air quality for the next day using airborne pollutant, meteorological and timing variables.Hourly airborne pollutant and meteorological averages collected during the years 1995–1997 were analysed in order to identify air quality episodes having typical and the most probable combinations of air pollutant and meteorological variables. This modelling was done using the Self-Organising Map (SOM) algorithm, Sammon's mapping and fuzzy distance metrics. The clusters of data that were found were characterised by statistics. Several overlapping Multi-Layer Perceptron (MLP) models were then applied to the clustered data, each of which represented one pollution episode. The actual levels for individual pollutants could then be calculated using a combination of the MLP models which were appropriate in that situation.The analysis phase of the modelling gave clear and intuitive results regarding air quality in the area where the data had been collected. The resulting forecast showed that the modelling of gaseous pollutants is more reliable than that of the particles.     

Smoke aerosols dispersion and transport from the 2013 New South Wales (Australia) bushfires

Environmental monitoring and modelling, especially in the regional context, has seen significant progress with the widely usage of satellite measurement in conjunction with local meteorological and air quality monitoring to understand the atmospheric dispersion and transport of air pollutants. This paper studies the application of these data and modelling tools to understand the environment effects of a major bushfire period in the state of New South Wales (NSW), Australia, in 2013. The bushfires have caused high pollution episodes at many sites in the greater Sydney metropolitan areas. The potential long-range transport of aerosols produced by bushfires to other region and states has been seen by regulators as a major concern. Using data and images collected from satellites, in addition to the results obtained from different simulations carried out using HYSPLIT trajectory model and a regional meteorological model called Conformal Cubic Atmospheric Model (CCAM), we were able to identify at least 2 days on which the smoke aerosols from bush fires in NSW has been transported at high altitude to the northern state of Queensland and the Coral Sea. As a result, widespread high particle concentration in South East Queensland including the Brisbane area, as measured by nearly all the air quality monitoring stations in this region, occurred on the day when the smoke aerosols intruded to lower altitude as indicated by the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) Lidar measurements on the CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. The use of meteorological or air quality modelling to connect the ground-based measurements with satellite observations as shown in this study is useful to understand the pollutant transport due to bushfires and its impact on regional air quality.     

Analyzing SO 2 concentrations and wind directions during a short monitoring campaign at a site far from the industrial pole of La Plata, Argentina

This article presents and discusses SO2 air quality concentrations (ppbv) together with wind velocities and directions measurements carried out between September 1st and December 21st 2005 at a site located 8.5 km away from the Industrial Pole of La Plata area. As the city and its surroundings have no official monitoring network, the current work enlarges the air quality information available from the zone and sets some initial considerations to the future siting of monitoring stations. The statistical analysis of the data was performed using techniques of tests for outliers and trends, dissimilarity measures and robust regression. In relation to SO2 concentrations, low values were found during this short campaign considering daily averages (with a maximum of 8.5 ppbv) and hourly averages (with a maximum of 25.9 ppbv); World Health Organization guidelines were never surpassed. Nevertheless, a strong dependence between wind directions carrying air pollutants from the Industrial Pole and hourly concentration peaks were found. Due to low monthly SO2 concentrations and because a decreasing time trend was found, the authors propose, as an example, the implementation of an alternative discontinuous method to the continuous analyzer used in the current campaign. Our results state that sampling every 7 days at 13:00-13:59 hours (local time) would be enough to get representative values of the air quality. As a general remark it is possible to highlight that longer and systematic studies should be encouraged to confirm the seasonal wind pattern and to evaluate the air quality.     

中小城市空气质量预报试验与问题研究

当前中国大气污染形势严峻,迫切需要开展环境空气质量预报预警业务工作。为调研国内中小城市开展空气质量预报的基础条件和业务需求,促进全国城市空气质量业务体系建设,中国环境监测总站组织8个典型中小城市,利用基于数值预报基础的统计模式预报系统开展了空气质量预报实验工作。在此基础上,针对开展全国预报业务体系建设所需的中小城市预报方法、基础条件、人员技术水平等方面进行专项调研,并对国内中小城市开展空气质量预报能力建设和业务工作面临的问题、困难提出了建议和参考对策。     

Short-Term Forecasting of Nitrogen Dioxide (NO<Subscript>2</Subscript>) Levels Using a Hybrid Statistical and Air Mass History Modelling Approach

A novel hybrid model has been developed to support the provision of real-time air quality forecasts. Statistical techniques have been applied in parallel with air mass history modelling to provide an efficient and accurate forecasting system with the ability to identify high NO₂ events, which tend to be the episodes of most significance in Ireland. Air mass history modelling and k-means clustering are used to identify air mass types that lead to high NO₂ levels in Ireland. Trajectory matching techniques allow data associated with these air masses to be partitioned during model development. Non-parametric regression (NPR) has been applied to describe nonlinear variations in concentration levels with wind speed, direction and season and produce a set of linearized factors which, together with other meteorological variables, are employed as inputs to a multiple linear regression. The model uses an innovative integrated approach to combine the NPR with the air mass history modelling results. On validation, a correlation coefficient of 0.75 was obtained, and 91 % of daily maximum (hourly averaged) NO₂ predictions were within a factor of two of the measured value. High pollution events were well captured, as indicated by strong agreement between measured and modelled high percentile values. The model requires only simple input data, does not require an emission inventory and utilises very low computational resources. It represents an accurate and efficient means of producing real-time air quality forecasts and, when used in combination with forecaster experience, is a useful tool for identifying periods of poor air quality 24 h in advance. The hybrid approach outlined in this paper can easily be applied to produce high-quality forecasts of both NO₂ and additional pollutants at new locations/countries where historical monitoring data are available.     

基于环境库兹涅茨曲线的中国城市环境空气质量主要影响因素

当前,中国城市环境空气污染形势十分严峻,空气质量呈现出典型的区域性特征。研究对2006—2012年各地区环境空气质量数据和经济社会发展指标统计资料面板数据进行分析,结果表明:研究选取时段内多数空气质量指标与人均国内生产总值之间的关系并不符合典型的环境库兹涅茨曲线(倒U型曲线),无显著相关性,但NO2质量浓度与人均国内生产总值之间呈现出倒N型曲线,空气质量综合指数与人口密度之间也呈现出倒N型曲线。空气质量综合指数与国民经济中第二产业占比和第三产业占比之间没有显著的相关关系,但与第一产业占比呈显著的负相关关系。空气质量综合指数与主要污染物单位面积排放量呈显著的正相关关系,与单位面积能源消费总量、单位面积煤炭消费量均呈显著的正相关关系,表明以煤炭为主要能源类型的能源消费带来的污染物排放是影响空气质量的主要因素。空气质量综合指数与降水量呈显著的负相关关系,降水量等气象条件对空气质量有一定影响,在开展大气污染防治时,应综合考虑各地的自然因素特征,合理确定工作目标和防治对策。     

PLS and PCR Methods in the Assessment of Coastal Water Quality

Partial least squares regression analysis (PLS) and principal component regression analysis (PCR) were examined asmethodological procedures for assessing the quality of coastalwaters in a tourist area. Four variables related to the trophicstate of waters, namely nitrate, nitrite, ammonia and phosphate were analyzed. The models resulting from PLS and PCR were verysimilar. Both defined three groups of water masses characterizedby different nutrient loadings. These groups were in accordancewith those obtained by numerical classification. The PLS methodwas selected as the optimal model, on the basis of its lowerprediction errors (lower Press and Rmsd values). For managementpurposes, this statistical model allows mesotrophic conditions,reflecting some nutrient enrichment over background conditions,to be characterized and the successful diagnosis of additionalsamples within this context.     

Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques

Acid mine drainage (AMD) is a global problem that may have serious human health and environmental implications. Laboratory and field tests are commonly used for predicting AMD, however, this is challenging since its formation varies from site-to-site for a number of reasons. Furthermore, these tests are often conducted at small-scale over a short period of time. Subsequently, extrapolation of these results into large-scale setting of mine sites introduce huge uncertainties for decision-makers. This study presents machine learning techniques to develop models to predict AMD quality using historical monitoring data of a mine site. The machine learning techniques explored in this study include artificial neural networks (ANN), support vector machine with polynomial (SVM-Poly) and radial base function (SVM-RBF) kernels, model tree (M5P), and K-nearest neighbors (K-NN). Input variables (physico-chemical parameters) that influence drainage dynamics are identified and used to develop models to predict copper concentrations. For these selected techniques, the predictive accuracy and uncertainty were evaluated based on different statistical measures. The results showed that SVM-Poly performed best, followed by the SVM-RBF, ANN, M5P, and KNN techniques. Overall, this study demonstrates that the machine learning techniques are promising tools for predicting AMD quality.     

Efficient control of air pollution through plants, a cost-effective alternative: studies on Dalbergia sissoo Roxb

Plants can be used as both passive biomonitors and biomitigators in urban and industrial environments to indicate the environmental quality and to ameliorate pollution level in a locality. Many studies reveal that plants are negatively affected by the ambient levels of air pollutants. The present study was conducted to evaluate the impact of air pollution on comparative basis with reference to changes in photosynthetic pigments, plant height, leaves, as well as, biochemical parameters of plants of different sites around Udaipur city receiving varying levels of pollution load. The investigated tree species Dalbergia sissoo Roxb. (Family: Fabaceae) exhibited a reduction in various physiological and biochemical growth parameters that correspond with air pollution levels at different sites. The tree species growing in polluted and control areas were compared with respect to foliar dust load, leaf area, and chlorophyll and total carbohydrate and total protein concentration in the leaves. Our studies suggest that D. sissoo Roxb. can successfully be grown in an area for monitoring air pollution, where it is mild and droughts are common. It will prove as an ideal tree species to control pollution effectively beside acting as a shade tree and being a source of food for birds and animals. By plantation of D. sissoo Roxb., mitigative measure at the polluted sites to control generation of particulate matter and the air quality required can be ensured. Our results also confirm that industrial and vehicular air pollution level in Udaipur city is shifting beyond limits.     

2016年秋季新乡市空气质量模式预报效果评估

基于新乡市空气质量数值预报平台，采用相关系数（r）、标准化平均偏差（NMB）等统计指标，系统评估2016年秋季新乡市嵌套网格空气质量预报模式（NAQPMS）和通用多尺度空气质量模式（CMAQ）的预报效果，对比分析2套模式不同预报时效和不同水平分辨率的空气质量等级预报准确率。结果显示：2套模式均较好地表征了各主要污染物的浓度变化特征，2套模式的等级预报准确率高于60%，其中CMAQ对中度及重度的预报等级准确率达到70%。对比模式24、48、72 h 3种预报时效效果，24 h预报时效的统计数据最优，说明24 h预报时效模拟结果可作为业务预报重要的支撑。     

宁夏总量减排与环境质量改善的相关性分析

利用SPSS统计软件分析宁夏“十一五”期间主要污染物总量控制指标与环境质量数据的关联程度,并建立回归模型.结果表明:宁夏总量减排与环境质量改善存在高度关联,拟合曲线均为一元线性回归曲线.随着SO2排放总量降低,重点城市空气质量稳步改善,两者呈现同向变化的趋势;随着COD排放总量降低,黄河宁夏段水环境质量稳步改善,两者亦呈现同向变化的趋势.宁夏总量减排在环境质量改善上成效已显现,污染防治政策较为成功,环保成绩显著.     

Simulation of PM<Subscript>10</Subscript> concentration patterns for a 2010 traffic scenario in Bologna,Italy

Three state of the art traffic–emission–dispersion models dealing with particulate matter have been tested and validated over the Bologna metropolitan area with 2001 data and a future scenario has been developed in order to estimate expected PM concentrations in 2010. The modelling system is composed by a traffic model (VISUM) evaluating vehicle fluxes as a function of mobility demand and road network in the area, an emission model (Trefic) estimating pollutants emitted in atmosphere as a function of vehicle fluxes amount and composition and of environmental conditions and a dispersion model (ADMS) evaluating PM concentrations on the area, given the meteorological variables. The three models compose a cascade sequence and results of the previous one feed the next one. PM concentrations computed by the model suite for the town of Bologna, in northern Italy, for the reference period (January 2001) have been compared with air quality stations measurements suggesting the modelling system being especially suitable for evaluating traffic induced PM. Qualitative and quantitative changes in the circulating vehicle fleet have been supposed in order to obtain a realistic scenario for year 2010. Forecasted concentrations have been then compared with limits fixed by current EU legislation for particulate matter.     

The Impact of Road Traffic in Madrid Community by Using a Nested Gaussian-Mesoscale Air Quality Model (GAMA)

In this contribution we show the integration of a mesoscale air quality model OPANA with the ISCST3 Gaussian model (EPA) in order to analyze the impact of different emission sources and particularly the traffic emission into the different gridboxes which define the OPANA Eulerian structure. The application is done over the Madrid (Spain) regional area with 80 × 100 km and gridboxes of about 5 km. Thousands of Gaussian runs over interested gridboxes are executed in order to simulate the traffic emissions from each gridbox. Each mobile unit is represented by a Gaussian point emitter. Input meteorological variables for the ISCST3 are taken from the OPANA mesoscale air quality model. Results shows that it is possible to model the impact of traffic emissions over each gridbox. A short comparison with air quality monitoring in each gridbox is also shown.     

沈阳市冬季环境空气质量统计预报模型建立及应用

利用沈阳市2013年1—2月大气自动监测数据和同期气象资料,选取19项预报因子,采用逐步回归方法建立了沈阳市冬季环境空气质量统计预报模型,预报项目包括细颗粒物(PM2.5)、可吸入颗粒物(PM10)、二氧化硫(SO2)、二氧化氮(NO2)、一氧化碳(CO)日均浓度及臭氧(O3)日最大8 h平均浓度。2013年11月至2014年1月,应用该模型并结合人为经验修订,开展了沈阳市环境空气质量预报工作,预报结果与实测结果的对比验证结果表明,环境空气预测结果级别准确率达到79.1%,首要污染物准确率为73.6%。     

Methodology for designing air quality monitoring networks: I. Theoretical aspects

An objective methodology is presented for determining the number and disposition of ambient air quality stations in a monitoring network for the primary purpose of compliance with air quality standards. The methodolgy utilizes a data base with real or simulated data from an air quality dispersion model for application with a two-step process for ascertaining the optimal monitoring network. In the first step, the air quality patterns in the data base are collapsed into a single composite pattern through a figure-of-merit (FOM) concept. The most desirable locations are ranked and identified using the resultant FOM fields. In the second step the network configuration is determined on the basis of the concept of spheres of influence (SOI) developed from cutoff values of spatial correlation coefficients between potential monitoring sites and adjacent locations. The minimum number of required stations is then determined by deletion of lower-ranked stations whose SOIs overlap. The criteria can be set to provide coverage of less than some fixed, user-provided percentage of the coverage of tha SOIs of the higher ranked stations and for some desired level of minimum detection capability of concentration fluctuations.The methodology is applied in a companion paper (McElroy et al., 1986) to the Las Vegas, Nevada, metropolitan area for the pollutant carbon monoxide.Although the research described in this article has been funded wholly or in part by the United States Environmental Protection Agency through Contract No. 68-03-2446 to Systems Applications, Inc., it has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

南京及周边区域亚青期间大气监控预警走航观测研究

2013年8月,为了解亚青会期间南京及周边地区大气污染物的来源、时空分布和输送规律,江苏省环境监测中心联合中科院大气物理所等15家单位,开展了为期一个月的夏季联合观测,监测区域覆盖江苏沿江8市以及安徽部分地区。该次联合观测,江苏首次利用空气质量数值预报模式指导开展走航观测,实现了动静、水平与垂直相结合的立体式巡航观测,并初步揭示了现阶段江苏省大气污染主要特征,观测结果直接服务于亚青空气质量会商,为保障南京市空气质量提供技术支撑,同时为2014青奥会空气质量保障积累经验。