Spatial analysis and land use regression of VOCs and NO2 from school-based urban air monitoring in Detroit/Dearborn, USA

Passive ambient air sampling for nitrogen dioxide (NO₂) and volatile organic compounds (VOCs) was conducted at 25 school and two compliance sites in Detroit and Dearborn, Michigan, USA during the summer of 2005. Geographic Information System (GIS) data were calculated at each of 116 schools. The 25 selected schools were monitored to assess and model intra-urban gradients of air pollutants to evaluate impact of traffic and urban emissions on pollutant levels. Schools were chosen to be statistically representative of urban land use variables such as distance to major roadways, traffic intensity around the schools, distance to nearest point sources, population density, and distance to nearest border crossing. Two approaches were used to investigate spatial variability. First, Kruskal-Wallis analyses and pairwise comparisons on data from the schools examined coarse spatial differences based on city section and distance from heavily trafficked roads. Secondly, spatial variation on a finer scale and as a response to multiple factors was evaluated through land use regression (LUR) models via multiple linear regression. For weeklong exposures, VOCs did not exhibit spatial variability by city section or distance from major roads; NO₂ was significantly elevated in a section dominated by traffic and industrial influence versus a residential section. Somewhat in contrast to coarse spatial analyses, LUR results revealed spatial gradients in NO₂ and selected VOCs across the area. The process used to select spatially representative sites for air sampling and the results of coarse and fine spatial variability of air pollutants provide insights that may guide future air quality studies in assessing intra-urban gradients.     

Source apportionment of indoor residential fine particulate matter using land use regression and constrained factor analysis

Source contributions to urban fine particulate matter (PM(2.5) ) have been modelled using land use regression (LUR) and factor analysis (FA). However, people spend more time indoors, where these methods are less explored. We collected 3-4- day samples of nitrogen dioxide and PM(2.5) inside and outside of 43 homes in summer and winter, 2003-2005, in and around Boston, Massachusetts. Particle filters were analysed for black carbon and trace element concentrations using reflectometry, X-ray fluorescence (XRF), and high-resolution inductively coupled mass spectrometry (ICP-MS). We regressed indoor against outdoor concentrations modified by ventilation, isolating the indoor-attributable fraction, and then applied constrained FA to identify source factors in indoor concentrations and residuals. Finally, we developed LUR predictive models using GIS-based outdoor source indicators and questionnaire data on indoor sources. FA using concentrations and residuals reasonably separated outdoor (long-range transport/meteorology, fuel oil/diesel, road dust) from indoor sources (combustion, smoking, cleaning). Multivariate LUR regression models for factors from concentrations and indoor residuals showed limited predictive power, but corroborated some indoor and outdoor factor interpretations. Our approach to validating source interpretations using LUR methods provides direction for studies characterizing indoor and outdoor source contributions to indoor cocentrations. PRACTICAL IMPLICATIONS: By merging indoor-outdoor modeling, factor analysis, and LUR-style predictive regression modeling, we have added to previous source apportionment studies by attempting to corroborate factor interpretations. Our methods and results support the possibility that indoor exposures may be modeled for epidemiologic studies, provided adequate sample size and variability to identify indoor and outdoor source contributions. Using these techniques, epidemiologic studies can more clearly examine exposures to indoor sources and indoor penetration of source-specific components, reduce exposure misclassification, and improve the characterization of the relationship between particle constituents and health effects.     

Particulate matter exposure along designated bicycle routes in Vancouver, British Columbia

An instrumented bicycle was used to elucidate particulate matter exposures along bicycle routes passing through a variety of land uses over 14 days during summer and fall in a mid-latitude traffic dominated urban setting. Overall, exposures were low or comparable to those found in studies elsewhere (mean PM(2.5) and PM(10) concentrations over each daily bicycle traverse varied between 7-34 microg m(-3) and 26-77 microg m(-3) respectively). Meteorological factors were responsible for significant day-to-day variability with PM(2.5) positively correlated with air temperature, PM(10) negatively correlated with precipitation, and ultrafine particles negatively correlated with both air temperature and wind speed. On individual days, land use and proximity to traffic were factors significantly affecting exposure along designated bicycle routes. While concentrations of PM(2.5) were found to be relatively spatially uniform over the length of the study route, PM(10) showed a more heterogeneous spatial distribution. Specifically, construction sites and areas susceptible to the suspension of road dust have higher concentrations of coarse particles. Ultrafine particles were also heterogeneously distributed in space, with areas with heavy traffic volumes having the highest concentrations. Observations show qualitative agreement in terms of spatial patterns with a land-use regression (LUR) model for annual PM(2.5) concentrations.     

Modelling air pollution for epidemiologic research--part II: predicting temporal variation through land use regression

Over recent years land use regression (LUR) has become a frequently used method in air pollution exposure studies, as it can model intra-urban variation in pollutant concentrations at a fine spatial scale. However, very few studies have used the LUR methodology to also model the temporal variation in air pollution exposure. The aim of this study is to estimate annual mean NO₂ and PM₁₀ concentrations from 1996 to 2008 for Greater Manchester using land use regression models. The results from these models will be used in the Manchester Asthma and Allergy Study (MAAS) birth cohort to determine health effects of air pollution exposure.The Greater Manchester LUR model for 2005 was recalibrated using interpolated and adjusted NO₂ and PM₁₀ concentrations as dependent variables for 1996-2008. In addition, temporally resolved variables were available for traffic intensity and PM₁₀ emissions. To validate the resulting LUR models, they were applied to the locations of automatic monitoring stations and the estimated concentrations were compared against measured concentrations.The 2005 LUR models were successfully recalibrated, providing individual models for each year from 1996 to 2008. When applied to the monitoring stations the mean prediction error (MPE) for NO₂ concentrations for all stations and years was -0.8 μg/m³ and the root mean squared error (RMSE) was 6.7 μg/m³. For PM₁₀ concentrations the MPE was 0.8 μg/m³ and the RMSE was 3.4 μg/m³.These results indicate that it is possible to model temporal variation in air pollution through LUR with relatively small prediction errors. It is likely that most previous LUR studies did not include temporal variation, because they were based on short term monitoring campaigns and did not have historic pollution data. The advantage of this study is that it uses data from an air dispersion model, which provided concentrations for 2005 and 2010, and therefore allowed extrapolation over a longer time period.     

A distance-decay variable selection strategy for land use regression modeling of ambient air pollution exposures

Land use regression (LUR) has emerged as an effective and economical means of estimating air pollution exposures for epidemiological studies. To date, no systematic method has been developed for optimizing the variable selection process. Traditionally, a limited number of buffer distances assumed having the highest correlations with measured pollutant concentrations are used in the manual stepwise selection process or a model transferred from another urban area.In this paper we propose a novel and systematic way of modeling long-term average air pollutant concentrations through “A Distance Decay REgression Selection Strategy” (ADDRESS). The selection process includes multiple steps and, at each step, a full spectrum of correlation coefficients and buffer distance decay curves are used to select a spatial covariate of the highest correlation (compared to other variables) at its optimized buffer distance. At the first step, the series of distance decay curves is constructed using the measured concentrations against the chosen spatial covariates. A variable with the highest correlation to pollutant levels at its optimized buffer distance is chosen as the first predictor of the LUR model from all the distance decay curves. Starting from the second step, the prediction residuals are used to construct new series of distance decay curves and the variable of the highest correlation at its optimized buffer distance is chosen to be added to the model. This process continues until a variable being added does not contribute significantly (p > 0.10) to the model performance. The distance decay curve yields a visualization of change and trend of correlation between the spatial covariates and air pollution concentrations or their prediction residuals, providing a transparent and efficient means of selecting optimized buffer distances. Empirical comparisons suggested that the ADDRESS method produced better results than a manual stepwise selection process of limited buffer distances. The method also enables researchers to understand the likely scale of variables that influence pollution levels, which has potentially important ramifications for planning and epidemiological studies.     

Ambient volatile organic compound (VOC) concentrations around a petrochemical complex and a petroleum refinery

Air samples were collected between September 2000 and September 2001 in Izmir, Turkey at three sampling sites located around a petrochemical complex and an oil refinery to measure ambient volatile organic compound (VOC) concentrations. VOC concentrations were 4-20-fold higher than those measured at a suburban site in Izmir, Turkey. Ethylene dichloride, a leaded gasoline additive used in petroleum refining and an intermediate product of the vinyl chloride process in the petrochemical complex, was the most abundant volatile organic compound, followed by ethyl alcohol and acetone. Evaluations based on wind direction clearly indicated that ambient VOC concentrations measured were affected by the refinery and petrochemical complex emissions. VOC concentrations showed seasonal variations at all sampling sites. Concentrations were highest in summer, followed by autumn, probably due to increased evaporation of VOCs from fugitive sources as a result of higher temperatures. VOC concentrations generally increased with temperature and wind speed. Temperature and wind speed together explained 1-60% of the variability in VOC concentrations. The variability in ambient VOC concentrations that could not be explained by temperature and wind speed can be attributed to the effect of other factors (i.e. wind direction, other VOC sources).     

基于地理国情普查数据的兰州市城关区土地开发建设适宜性评价研究

通过对适宜性评价模型和GIS相结合进行土地开发建设适宜性评价方法的研究,选取城关区内的评价指标因子,如地形坡度、地貌、水系、道路缓冲区、土地利用等,利用适宜性评价模型评价土地开发建设适宜性。基于GIS系统的空间分析功能,实现对空间数据叠加分析处理及评价结果图的输出,得到研究区土地开发建设适宜性评价结果,以期更准确揭示研究区土地开发建设规律,为区域城镇用地空间拓展、国土空间布局优化提供参考。     

Modelling air pollution for epidemiologic research — Part I: A novel approach combining land use regression and air dispersion

A common limitation of epidemiological studies on health effects of air pollution is the quality of exposure data available for study participants. Exposure data derived from urban monitoring networks is usually not adequately representative of the spatial variation of pollutants, while personal monitoring campaigns are often not feasible, due to time and cost restrictions. Therefore, many studies now rely on empirical modelling techniques, such as land use regression (LUR), to estimate pollution exposure. However, LUR still requires a quantity of specifically measured data to develop a model, which is usually derived from a dedicated monitoring campaign. A dedicated air dispersion modelling exercise is also possible but is similarly resource and data intensive.This study adopted a novel approach to LUR, which utilised existing data from an air dispersion model rather than monitored data. There are several advantages to such an approach such as a larger number of sites to develop the LUR model compared to monitored data. Furthermore, through this approach the LUR model can be adapted to predict temporal variation as well as spatial variation. The aim of this study was to develop two LUR models for an epidemiologic study based in Greater Manchester by using modelled NO₂ and PM₁₀ concentrations as dependent variables, and traffic intensity, emissions, land use and physical geography as potential predictor variables. The LUR models were validated through a set aside “validation” dataset and data from monitoring stations.The final models for PM₁₀ and NO₂ comprised nine and eight predictor variables respectively and had determination coefficients (R²) of 0.71 (PM₁₀: Adj. R² = 0.70, F = 54.89, p < 0.001, NO₂: Adj. R² = 0.70, F = 62.04, p < 0.001). Validation of the models using the validation data and measured data showed that the R² decreases compared to the final models, except for NO₂ validation in the measured data (validation data: PM₁₀: R² = 0.33, NO₂: R² = 0.62; measured data: PM₁₀: R² = 0.56, NO₂: R² = 0.86). The validation further showed low mean prediction errors and root mean squared errors for both models.     

FACTORS AFFECTING MICROBIOLOGICAL WATER QUALITY AT SIXTEEN BEACHES IN SOUTH-WEST WALES

Routine monitoring data from 1993 to 1999 were analysed to elucidate relationships between microbiological water quality and enviranmental conditions at sixteen EC identified bathing waters in South-West Wales. The objective was (a) to gain an understanding of the factors affecting non-compliance with the guideline standard of the EC Bathing Waters Directive, (b) to aid the development of action plans for improved bathing-water quality, and (c) to enable effective targeting of future investigations. The analyses demonstrated relationships between water quality and rainfall, sunshine, tidal range, tidal state, time of sampling, time of year, wind speed, wind direction, state of sea, transparency, river flows, river quality, salinity and temperature. The temporal and spatial variability in water quality shown by this study also highlights the need to ensure that monitoring programmes represent conditions at the times and locations of greatest bathing-water use.     

A land-use regression model for estimating microenvironmental diesel exposure given multiple addresses from birth through childhood

The Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS) is a prospective birth cohort whose purpose is to determine if exposure to high levels of diesel exhaust particles (DEP) during early childhood increases the risk for developing allergic diseases. In order to estimate exposure to DEP, a land-use regression (LUR) model was developed using geographic data as independent variables and sampled levels of a marker of DEP as the dependent variable. A continuous wind direction variable was also created. The LUR model predicted 74% of the variability in sampled values with four variables: wind direction, length of bus routes within 300 m of the sample site, a measure of truck intensity within 300 m of the sampling site, and elevation. The LUR model was subsequently applied to all locations where the child had spent more than eight hours per week from through age three. A time-weighted average (TWA) microenvironmental exposure estimate was derived for four time periods: 0-6 months, 7-12 months, 13-24 months, 25-36 months. By age two, one third of the children were spending significant time at locations other than home and by 36 months, 39% of the children had changed their residential addresses. The mean cumulative DEP exposure estimate increased from age 6 to 36 months from 70 to 414 mug/m(3)-days. Findings indicate that using birth addresses to estimate a child's exposure may result in exposure misclassification for some children who spend a significant amount of time at a location with high exposure to DEP.     

The impact of water consumption, point-of-use filtration and exposure categorization on exposure misclassification of ingested drinking water contaminants

The use of population-level indices to estimate individual exposures is an important limitation of previous epidemiologic studies of disinfection by-products (DBPs). We examined exposure misclassification resulting from the use of system average DBP concentrations to estimate individual-level exposures. Data were simulated (n=1000 iterations) for 100 subjects across 10 water systems based on the following assumptions: DBP concentrations ranged from 0-99 microg/L with limited intra-system variability; water intake ranged from 0.5-2.5 L/day; 20% of subjects used bottled water exclusively; 20% of subjects used filtered tap water exclusively; DBP concentrations were reduced by 50% or 90% following filtration. DBP exposure percentiles were used to classify subjects into different exposure levels (e.g., low, intermediate, high and very high) for four classification approaches. Compared to estimates of DBP ingestion that considered daily consumption, source type (i.e., unfiltered tap, filtered tap, and bottled water), and filter efficiency (with 90% DBP removal), 48-62% of subjects were misclassified across one category based on system average concentrations. Average misclassification across at least two exposure categories (e.g., from high to low) ranged from 4-14%. The median classification strategy resulted in the least misclassification, and volume of water intake was the most influential modifier of ingestion exposures. These data illustrate the importance of individual water use information in minimizing exposure misclassification in epidemiologic studies of drinking water contaminants.     

A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments

Accurate, high-resolution maps of traffic-related air pollution are needed both as a basis for assessing exposures as part of epidemiological studies, and to inform urban air-quality policy and traffic management. This paper assesses the use of a GIS-based, regression mapping technique to model spatial patterns of traffic-related air pollution. The model--developed using data from 80 passive sampler sites in Huddersfield, as part of the SAVIAH (Small Area Variations in Air Quality and Health) project--uses data on traffic flows and land cover in the 300-m buffer zone around each site, and altitude of the site, as predictors of NO2 concentrations. It was tested here by application in four urban areas in the UK: Huddersfield (for the year following that used for initial model development), Sheffield, Northampton, and part of London. In each case, a GIS was built in ArcInfo, integrating relevant data on road traffic, urban land use and topography. Monitoring of NO2 was undertaken using replicate passive samplers (in London, data were obtained from surveys carried out as part of the London network). In Huddersfield, Sheffield and Northampton, the model was first calibrated by comparing modelled results with monitored NO2 concentrations at 10 randomly selected sites; the calibrated model was then validated against data from a further 10-28 sites. In London, where data for only 11 sites were available, validation was not undertaken. Results showed that the model performed well in all cases. After local calibration, the model gave estimates of mean annual NO2 concentrations within a factor of 1.5 of the actual mean (approx. 70-90%) of the time and within a factor of 2 between 70 and 100% of the time. r2 values between modelled and observed concentrations are in the range of 0.58-0.76. These results are comparable to those achieved by more sophisticated dispersion models. The model also has several advantages over dispersion modelling. It is able, for example, to provide high-resolution maps across a whole urban area without the need to interpolate between receptor points. It also offers substantially reduced costs and processing times compared to formal dispersion modelling. It is concluded that the model might thus be used as a means of mapping long-term air pollution concentrations either in support of local authority air-quality management strategies, or in epidemiological studies.     

Dependence of urban air pollutants on meteorology

Dependence of air pollutants on meteorology is presented with the aim of understanding the governing processes pollutants phase interaction. Intensive measurements of particulate matter (PM10) and gaseous materials (e.g., CO, NO2, SO2, and O3) are carried out regularly in 2002 at 14 measurement sites distributed over the whole territory of Great Cairo by the Egyptian Environmental Affairs Agency to assess the characteristics of air pollutants. The discussions in this work are based upon measurements performed at Abbassiya site as a case study. The nature of the contributing sources has been investigated and some attempts have been made to indicate the role played by neighboring regions in determining the air quality at the site mentioned. The results hint that, wind direction was found to have an influence not only on pollutant concentrations but also on the correlation between pollutants. As expected, the pollutants associated with traffic were at highest ambient concentration levels when wind speed was low. At higher wind speeds, dust and sand from the surrounding desert was entrained by the wind, thus contributing to ambient particulate matter levels. We also found that, the highest average concentration for NO2 and O3 occurred at humidity相似文献   

Predicting personal exposure of pregnant women to traffic-related air pollutants

As epidemiological studies report associations between ambient air pollution and adverse birth outcomes, it is important to understand determinants of exposures among pregnant women. We measured (48-h, personal exposure) and modeled (using outdoor ambient monitors and a traffic-based land-use regression model) NO, NO(2), fine particle mass and absorbance in 62 non-smoking pregnant women in Vancouver, Canada on 1-3 occasions during pregnancy (total N=127). We developed predictive models for personal measurements using modeled ambient concentrations and individual determinants of exposure. Geometric mean exposures of personal samples were relatively low (GM (GSD) NO=37 ppb (2.0); NO(2)=17 ppb (1.6); 'soot', as filter absorbance=0.8 10(-5) m(-1) (1.5); PM(2.2)=10 microg m(-3) (1.6)). Having a gas stove (vs. electric stove) in the home was associated with exposure increases of 89% (NO), 44% (NO(2)), 20% (absorbance) and 35% (fine PM). Interpolated concentrations from outdoor fixed-site monitors were associated with all personal exposures except NO(2). Land-use regression model estimates of outdoor air pollution were associated with personal NO and NO(2) only. The effects of outdoor air pollution on personal samples were consistent, with and without adjustment for other individual determinants (e.g. gas stove). These findings improve our understanding of sources of exposure to air pollutants among pregnant women and support the use of outdoor concentration estimates as proxies for exposure in epidemiologic studies.     

Application of nonparametric regression methods to study the relationship between NO2 concentrations and local wind direction and speed at background sites

Background concentrations of nitrogen dioxide (NO₂) are not constant but vary temporally and spatially. The current paper presents a powerful tool for the quantification of the effects of wind direction and wind speed on background NO₂ concentrations, particularly in cases where monitoring data are limited. In contrast to previous studies which applied similar methods to sites directly affected by local pollution sources, the current study focuses on background sites with the aim of improving methods for predicting background concentrations adopted in air quality modelling studies. The relationship between measured NO₂ concentration in air at three such sites in Ireland and locally measured wind direction has been quantified using nonparametric regression methods. The major aim was to analyse a method for quantifying the effects of local wind direction on background levels of NO₂ in Ireland. The method was expanded to include wind speed as an added predictor variable. A Gaussian kernel function is used in the analysis and circular statistics employed for the wind direction variable. Wind direction and wind speed were both found to have a statistically significant effect on background levels of NO₂ at all three sites. Frequently environmental impact assessments are based on short term baseline monitoring producing a limited dataset. The presented non-parametric regression methods, in contrast to the frequently used methods such as binning of the data, allow concentrations for missing data pairs to be estimated and distinction between spurious and true peaks in concentrations to be made. The methods were found to provide a realistic estimation of long term concentration variation with wind direction and speed, even for cases where the data set is limited. Accurate identification of the actual variation at each location and causative factors could be made, thus supporting the improved definition of background concentrations for use in air quality modelling studies.     

A sub-neighborhood scale land use regression model for predicting NO(2)

This study set out to develop a land use regression model at sub-neighborhood scale (0.01-1 km) for Portland, Oregon using passive measurements of NO(2) at 77 locations. Variables used to develop the model included road and railroad density, traffic volume, and land use with buffers of 50 to 750 m surrounding each measurement site. An initial regression model was able to predict 66% of the variation in NO(2). Including wind direction in the regression model increased predictive power by 15%. Iterative random exclusion of 11 sites during model calibration resulted in a 3% variation in predictive power. The regression model was applied to the Portland metropolitan area using 10 m gridded land use layers. This study further validates land use regression for use in North America, and identifies important considerations for their use, such as inclusion of railways, open spaces and meteorological patterns.     

A driving rain exposure index for Norway

Determination of driving rain exposure typically requires hourly values of rainfall and mean directional wind speed. Weather data at most observing stations in Norway are not recorded as hourly values and are therefore not amenable to this type of analysis. We present an alternative method for assessing driving rain exposures based on multi-year records of synoptic observations of present weather, wind speed and direction. Distributions of numbers of rain observations and wind speeds versus wind direction combined with average annual rainfall totals yield quantitative information about driving rain exposures at stations. Results from four weather stations in Norway are presented and discussed, using weather data from the period 1974–2003.     

Revealing the relationship of human convergence–divergence patterns and land use: A case study on Shenzhen City,China

Understanding the relationship between human mobility and land use has been a longstanding topic in multiple disciplines, including transport geography and urban planning. Recently, urban collective mobility patterns have become a hot research direction and has been explored at an unprecedented space–time scale due to the emerging big human tracking datasets (e.g., mobile phone data). However, only a few studies have comprehensively quantified the effects of land use on human mobility patterns while considering the influence of the scale of spatial analysis units. This study attempts to reinforce this knowledge by investigating urban human convergence–divergence patterns and their relationship with land use distribution characteristics at three popular types of spatial analysis units of human mobility studies (voronoi polygons, grid cells, and traffic analysis zones) using mobile phone data. A case study on Shenzhen, China is implemented, and results indicate that eight distinct convergence–divergence patterns could be extracted to describe urban collective mobility patterns despite the use of different types of spatial analysis unit. Moreover, the scale of spatial analysis units exerts a few effects on the quantification of the influence of land use distribution on human convergence–divergence patterns, but some common characteristics could be summarized from these discrepant results. The findings can help policy makers understand urban human mobility and can serve as a guide for urban management and planning.     

Automated extraction and classification of thunderstorm and non-thunderstorm wind data for extreme-value analysis

Design wind loads are partly based on extreme value analyses of historical wind data, and limitations on the quantity and spatial resolution of wind data pose a significant challenge in such analyses. A promising source of recent wind speed and direction data is the automated surface observing system (ASOS), a network of about 1000 standardized US weather stations. To facilitate the use of ASOS data for structural engineering purposes, procedures and software are presented for (a) extraction of peak gust wind data and thunderstorm observations from archived ASOS reports, (b) classification of wind data as thunderstorm or non-thunderstorm to enable separate analyses, and (c) construction of data sets separated by specified minimum time intervals to ensure statistical independence. The procedures are illustrated using approximately 20-year datasets from three ASOS stations near New York City. It is shown that for these stations thunderstorm wind speeds dominate the extreme wind climate at long return periods. Also presented are estimates based on commingled data sets (i.e., sets containing, indiscriminately, both non-thunderstorm and thunderstorm wind speeds), which until now have been used almost exclusively for extreme wind speed estimates in the US. Analyses at additional stations will be needed to check whether these results are typical for locations with both thunderstorm and non-thunderstorm winds.     

南通城市空间结构演变过程与特征研究

借助GIS软件支持,综合遥感影像和其他空间数据,提取南通市1994年和2008年建设用地图形信息,对南通市自建城设州以来的城市空间结构演变过程进行了分析,用传统拓扑分析技术研究南通市拓扑空间特征,介绍了南通市市区建设用地现状,从数量、速度、结构和布局方面对南通市城市空间结构演变特征作了总结。