Comparison of the performances of land use regression modelling and dispersion modelling in estimating small-scale variations in long-term air pollution concentrations in a Dutch urban area

The performance of a Land Use Regression (LUR) model and a dispersion model (URBIS – URBis Information System) was compared in a Dutch urban area. For the Rijnmond area, i.e. Rotterdam and surroundings, nitrogen dioxide (NO₂) concentrations for 2001 were estimated for nearly 70 000 centroids of a regular grid of 100 × 100 m.A LUR model based upon measurements carried out on 44 sites from the Dutch national monitoring network and upon Geographic Information System (GIS) predictor variables including traffic intensity, industry, population and residential land use was developed. Interpolation of regional background concentration measurements was used to obtain the regional background. The URBIS system was used to estimate NO₂ concentrations using dispersion modelling. URBIS includes the CAR model (Calculation of Air pollution from Road traffic) to calculate concentrations of air pollutants near urban roads and Gaussian plume models to calculate air pollution levels near motorways and industrial sources. Background concentrations were accounted for using 1 × 1 km maps derived from monitoring and model calculations.Moderate agreement was found between the URBIS and LUR in calculating NO₂ concentrations (R = 0.55). The predictions agreed well for the central part of the concentration distribution but differed substantially for the highest and lowest concentrations. The URBIS dispersion model performed better than the LUR model (R = 0.77 versus R = 0.47 respectively) in the comparison between measured and calculated concentrations on 18 validation sites. Differences can be understood because of the use of different regional background concentrations, inclusion of rather coarse land use category industry as a predictor variable in the LUR model and different treatment of conversion of NO to NO₂.Moderate agreement was found between a dispersion model and a land use regression model in calculating annual average NO₂ concentrations in an area with multiple sources. The dispersion model explained concentrations at validation sites better.     

Back-extrapolation of estimates of exposure from current land-use regression models

Land use regression has been used in epidemiologic studies to estimate long-term exposure to air pollution within cities. The models are often developed toward the end of the study using recent air pollution data. Given that there may be spatially-dependent temporal trends in urban air pollution and that there is interest for epidemiologists in assessing period-specific exposures, especially early-life exposure, methods are required to extrapolate these models back in time. We present herein three new methods to back-extrapolate land use regression models. During three two-week periods in 2005–2006, we monitored nitrogen dioxide (NO₂) at about 130 locations in Montreal, Quebec, and then developed a land-use regression (LUR) model. Our three extrapolation methods entailed multiplying the predicted concentrations of NO₂ by the ratio of past estimates of concentrations from fixed-site monitors, such that they reflected the change in the spatial structure of NO₂ from measurements at fixed-site monitors. The specific methods depended on the availability of land use and traffic-related data, and we back-extrapolated the LUR model to 10 and 20 years into the past. We then applied these estimates to residential information from subjects enrolled in a case–control study of postmenopausal breast cancer that was conducted in 1996.Observed and predicted concentrations of NO₂ in Montreal decreased and were correlated in time. The estimated concentrations using the three extrapolation methods had similar distributions, except that one method yielded slightly lower values. The spatial distributions varied slightly between methods. In the analysis of the breast cancer study, the odds ratios were insensitive to the method but varied with time: for a 5 ppb increase in NO₂ using the 2006 LUR the odds ratio (OR) was about 1.4 and the ORs in predicted past concentrations of NO₂ varied (OR～1.2 for 1985 and OR～1.3–1.5 for 1996). Thus, the ORs per unit exposure increased with time as the range and variance of the spatial distributions decreased, and this is due partly to the regression coefficient being approximately inversely proportional to the variance of exposure. Changing spatial variability complicates interpretation and this may have important implications for the management of risk. Further studies are needed to estimate the accuracy of the different methods.     

The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies

A methodology is developed to include wind flow effects in land use regression (LUR) models for predicting nitrogen dioxide (NO₂) concentrations for health exposure studies. NO₂ is widely used in health studies as an indicator of traffic-generated air pollution in urban areas. Incorporation of high-resolution interpolated observed wind direction from a network of 38 weather stations in a LUR model improved NO₂ concentration estimates in densely populated, high traffic and industrial/business areas in Toronto-Hamilton urban airshed (THUA) of Ontario, Canada. These small-area variations in air pollution concentrations that are probably more important for health exposure studies may not be detected by sparse continuous air pollution monitoring network or conventional interpolation methods. Observed wind fields were also compared with wind fields generated by Global Environmental Multiscale-High resolution Model Application Project (GEM-HiMAP) to explore the feasibility of using regional weather forecasting model simulated wind fields in LUR models when observed data are either sparse or not available. While GEM-HiMAP predicted wind fields well at large scales, it was unable to resolve wind flow patterns at smaller scales. These results suggest caution and careful evaluation of regional weather forecasting model simulated wind fields before incorporating into human exposure models for health studies. This study has demonstrated that wind fields may be integrated into the land use regression framework. Such integration has a discernable influence on both the overall model prediction and perhaps more importantly for health effects assessment on the relative spatial distribution of traffic pollution throughout the THUA. Methodology developed in this study may be applied in other large urban areas across the world.     

Evaluation of land-use regression models used to predict air quality concentrations in an urban area

Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations in order to minimize exposure misclassification errors. However, it is often difficult to collect concentration information at each study subject location. In the absence of complete subject-specific measurements, land-use regression (LUR) models have frequently been used for estimating individual levels of exposures to ambient air pollution. The LUR models, however, have several limitations mainly dealing with extensive monitoring data needs and challenges involved in their broader applicability to other locations. In contrast, air quality models can provide high-resolution source–concentration linkages for multiple pollutants, but require detailed emissions and meteorological information. In this study, first we predicted air quality concentrations of PM_2.5, NO_x, and benzene in New Haven, CT using hybrid modeling techniques based on CMAQ and AERMOD model results. Next, we used these values as pseudo-observations to develop and evaluate the different LUR models built using alternative numbers of (training) sites (ranging from 25 to 285 locations out of the total 318 receptors). We then evaluated the fitted LUR models using various approaches, including: 1) internal “Leave-One-Out-Cross-Validation” (LOOCV) procedure within the “training” sites selected; and 2) “Hold-Out” evaluation procedure, where we set aside 33–293 tests sites as independent datasets for external model evaluation. LUR models appeared to perform well in the training datasets. However, when these LUR models were tested against independent hold out (test) datasets, their performance diminished considerably. Our results confirm the challenges facing the LUR community in attempting to fit empirical response surfaces to spatially- and temporally-varying pollution levels using LUR techniques that are site dependent. These results also illustrate the potential benefits of enhancing basic LUR models by utilizing air quality modeling tools or concepts in order to improve their reliability or transferability.     

Determinants of perceived air pollution annoyance and association between annoyance scores and air pollution (PM2.5, NO2) concentrations in the European EXPOLIS study

Apart from its traditionally considered objective impacts on health, air pollution can also have perceived effects, such as annoyance. The psychological effects of air pollution may often be more important to well-being than the biophysical effects. Health effects of perceived annoyance from air pollution are so far unknown. More knowledge of air pollution annoyance levels, determinants and also associations with different air pollution components is needed. In the European air pollution exposure study, EXPOLIS, the air pollution annoyance as perceived at home, workplace and in traffic were surveyed among other study objectives. Overall 1736 randomly drawn 25–55-yr-old subjects participated in six cities (Athens, Basel, Milan, Oxford, Prague and Helsinki). Levels and predictors of individual perceived annoyances from air pollution were assessed. Instead of the usual air pollution concentrations at fixed monitoring sites, this paper compares the measured microenvironment concentrations and personal exposures of PM_2.5 and NO₂ to the perceived annoyance levels. A considerable proportion of the adults surveyed was annoyed by air pollution. Female gender, self-reported respiratory symptoms, downtown living and self-reported sensitivity to air pollution were directly associated with high air pollution annoyance score while in traffic, but smoking status, age or education level were not significantly associated. Population level annoyance averages correlated with the city average exposure levels of PM_2.5 and NO₂. A high correlation was observed between the personal 48-h PM_2.5 exposure and perceived annoyance at home as well as between the mean annoyance at work and both the average work indoor PM_2.5 and the personal work time PM_2.5 exposure. With the other significant determinants (gender, city code, home location) and home outdoor levels the model explained 14% (PM_2.5) and 19% (NO₂) of the variation in perceived air pollution annoyance in traffic. Compared to Helsinki, in Basel and Prague the adult participants were more annoyed by air pollution while in traffic even after taking the current home outdoor PM_2.5 and NO₂ levels into account.     

Assessment of contribution of SO2 and NO2 from different sources in Jamshedpur region, India

Contribution of pollution from different types of sources in Jamshedpur, the steel city of India, has been estimated in winter 1993 using two approaches in order to delineate and prioritize air quality management strategies for the development of region in an environmental friendly manner. The first approach mainly aims at preparation of a comprehensive emission inventory and estimation of spatial distribution of pollution loads in terms of SO₂ and NO₂ from different types of industrial, domestic and vehicular sources in the region. The results indicate that industrial sources account for 77% and 68% of the total emissions of SO₂ and NO₂, respectively, in the region, whereas vehicular emissions contributed to about 28% of the total NO₂ emissions. In the second approach, contribution of these sources to ambient air quality levels to which the people are exposed to, was assessed through air pollution dispersion modelling. Ambient concentration levels of SO₂ and NO₂ have been predicted in winter season using the ISCST3 model. The analysis indicates that emissions from industrial sources are responsible for more than 50% of the total SO₂ and NO₂ concentration levels. Vehicular activities contributed to about 40% of NO₂ pollution and domestic fuel combustion contributed to about 38% of SO₂ pollution. Predicted 24-h concentrations were compared with measured concentrations at 11 ambient air monitoring stations and good agreement was noted between the two values. In-depth zone-wise analysis of the above indicates that for effective air quality management, industrial source emissions should be given highest priority, followed by vehicular and domestic sources in Jamshedpur region.     

Comparison of land-use regression models between Great Britain and the Netherlands

Land-use regression models have increasingly been applied for air pollution mapping at typically the city level. Though models generally predict spatial variability well, the structure of models differs widely between studies. The observed differences in the models may be due to artefacts of data and methodology or underlying differences in source or dispersion characteristics. If the former, more standardised methods using common data sets could be beneficial. We compared land-use regression models for NO₂ and PM_10, developed with a consistent protocol in Great Britain (GB) and the Netherlands (NL).Models were constructed on the basis of 2001 annual mean concentrations from the national air quality networks. Predictor variables used for modelling related to traffic, population, land use and topography. Four sets of models were developed for each country. First, predictor variables derived from data sets common to both countries were used in a pooled analysis, including an indicator for country and interaction terms between country and the identified predictor variables. Second, the common data sets were used to develop individual baseline models for each country. Third, the country-specific baseline models were applied after calibration in the other country to explore transferability. The fourth model was developed using the best possible predictor variables for each country.A common model for GB and NL explained NO₂ concentrations well (adjusted R² 0.64), with no significant differences in intercept and slopes between the two countries. The country-specific model developed on common variables for NL but not GB improved the prediction.The performance of models based upon common data was only slightly worse than models optimised with local data. Models transferred to the other country performed substantially worse than the country-specific models. In conclusion, care is needed both in transferring models across different study areas, and in developing large inter-regional LUR models.     

A prediction-based approach to modelling temporal and spatial variability of traffic-related air pollution in Montreal, Canada

Concentrations of traffic-related air pollution can be highly variable at the local scale and can have substantial seasonal variability. This study was designed to provide estimates of intra-urban concentrations of ambient nitrogen dioxide (NO₂) in Montreal, Canada, that would be used subsequently in health studies of chronic diseases and long-term exposures to traffic-related air pollution. We measured concentrations of NO₂ at 133 locations in Montreal with passive diffusion samplers in three seasons during 2005 and 2006. We then used land use regression, a proven statistical prediction method for describing spatial patterns of air pollution, to develop separate estimates of spatial variability across the city by regressing NO₂ against available land-use variables in each of these three periods. We also developed a “pooled” model across these sampling periods to provide an estimate of an annual average. Our modelling strategy was to develop a predictive model that maximized the model R². This strategy is different from other strategies whose goal is to identify causal relationships between predictors and concentrations of NO₂.Observed concentrations of NO₂ ranged from 2.6 ppb to 31.5 ppb, with mean values of 12.6 ppb in December 2005, 14.0 ppb in May 2006, and 8.9 ppb in August 2006. The greatest variability was observed during May. Concentrations of NO₂ were highest downtown and near major highways, and they were lowest in the western part of the city. Our pooled model explained approximately 80% of the variability in concentrations of NO₂. Although there were differences in concentrations of NO₂ between the three sampling periods, we found that the spatial variability did not vary significantly across the three sampling periods and that the pooled model was representative of mean annual spatial patterns.     

Estimated long-term outdoor air pollution concentrations in a cohort study

Several recent studies associated long-term exposure to air pollution with increased mortality. An ongoing cohort study, the Netherlands Cohort Study on Diet and Cancer (NLCS), was used to study the association between long-term exposure to traffic-related air pollution and mortality. Following on a previous exposure assessment study in the NLCS, we improved the exposure assessment methods.Long-term exposure to nitrogen dioxide (NO₂), nitrogen oxide (NO), black smoke (BS), and sulphur dioxide (SO₂) was estimated. Exposure at each home address (N=21 868) was considered as a function of a regional, an urban and a local component. The regional component was estimated using inverse distance weighed interpolation of measurement data from regional background sites in a national monitoring network. Regression models with urban concentrations as dependent variables, and number of inhabitants in different buffers and land use variables, derived with a Geographic Information System (GIS), as predictor variables were used to estimate the urban component. The local component was assessed using a GIS and a digital road network with linked traffic intensities. Traffic intensity on the nearest road and on the nearest major road, and the sum of traffic intensity in a buffer of 100 m around each home address were assessed. Further, a quantitative estimate of the local component was estimated.The regression models to estimate the urban component explained 67%, 46%, 49% and 35% of the variances of NO₂, NO, BS, and SO₂ concentrations, respectively. Overall regression models which incorporated the regional, urban and local component explained 84%, 44%, 59% and 56% of the variability in concentrations for NO₂, NO, BS and SO₂, respectively.We were able to develop an exposure assessment model using GIS methods and traffic intensities that explained a large part of the variations in outdoor air pollution concentrations.     

An assessment of the impact of climate change on air quality at two UK sites

Possible effects of climate change on air quality are studied for two urban sites in the UK, London and Glasgow. Hourly meteorological data were obtained from climate simulations for two periods representing the current climate and a plausible late 21st century climate. Of the meteorological quantities relevant to air quality, significant changes were found in temperature, specific humidity, wind speed, wind direction, cloud cover, solar radiation, surface sensible heat flux and precipitation. Using these data, dispersion estimates were made for a variety of single sources and some significant changes in environmental impact were found in the future climate. In addition, estimates for future background concentrations of NO_x, NO₂, ozone and PM₁₀ upwind of London and Glasgow were made using the meteorological data in a statistical model. These showed falls in NO_x and increases in ozone for London, while a fall in NO₂ was the largest percentage change for Glasgow. Other changes were small. With these background estimates, annual-average concentrations of NO_x, NO₂, ozone and PM₁₀ were estimated within the two urban areas. For London, results averaged over a number of sites showed a fall in NO_x and a rise in ozone, but only small changes in NO₂ and PM₁₀. For Glasgow, the changes in all four chemical species were small. Large-scale background ozone values from a global chemical transport model are also presented. These show a decrease in background ozone due to climate change. To assess the net impact of both large scale and local processes will require models which treat all relevant scales.     

Regression modelling of hourly NOx and NO2 concentrations in urban air in London

Based on hourly measurements of NO_x NO₂ and O₃ and meteorological data, an ordinary least squares (OLS) model and a first-order autocorrelation (AR) model were developed to analyse the regression and prediction of NO_x and NO₂ concentrations in London. Primary emissions and wind speed are the most important factors influencing NO_x concentrations; in addition to these two, reaction of NO with O₃ is also a major factor influencing NO₂ concentrations. The AR model resulted in high correlation coefficients (R > 0.95) for the NO_x and NO₂ regression based on a whole year's data, and is capable of predicting NO₂ (R = 0.83) and NO_x (R = 0.65) concentrations when the explanatory variables were available. The analysis of the structure of regression models by Principal Component Analysis (PCA) indicates that the regression models are stable. The results of the OLS model indicate that there was an exceptional NO₂ source, other than primary emission and reaction of NO with O₃, in the air pollution episode in London in December 1991.     

Analysis of model responses to emission-reduction scenarios within the CityDelta project

This paper investigates how air quality models applied at different scales (50 and 5 km horizontal resolutions) can predict pollution levels in response to emission control strategies in various cities in Europe. This study, involving five modelling teams and focused on four European cities, has been conducted within the CityDelta project (http://aqm.jrc.it/citydelta). The CityDelta models generally agree, on the O₃ changes expected from scenarios representative of the current legislation on air pollution in 2010, named CLE. They also agree about less scope for further improvements from emission controls beyond CLE. For PM₁₀, more significant differences between the models are observed, especially between models with different spatial resolutions. However, these differences are city-dependent and are larger in complex geographical areas such as Milan in the Pô Valley than in the Paris area.Fine scale models generally capture important urban scale effects, which are not represented by regional scale models. For instance, they improve the simulation of potential O₃ increase caused by NO_x emissions reduction in NMVOC-limited regime situations. Large scale models generally underpredict PM mean concentrations in city areas. A series of emission scenarios to address the question of the efficiency of local emission controls designed independently from regional measures is analyzed. The analysis of the CityDelta results contributes to the quantification of the impact of grid resolution in air quality modelling, and its application to emission control scenarios.     

Characteristics and origins of air pollutants in Wuhan,China, based on observations and hybrid receptor models

To identify the characteristics of air pollutants and factors attributing to the formation of haze in Wuhan, this study analyzed the hourly observations of air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO) from March 1, 2013, to February 28, 2014, and used hybrid receptor models for a case study. The results showed that the annual average concentrations for PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO during the whole period were 89.6 μg m^?3, 134.9 μg m^?3, 54.9 μg m^?3, 32.4 μg m^?3, 62.3 μg m^?3, and 1.1 mg m^?3, respectively. The monthly variations revealed that the peak values of PM_2.5, PM₁₀, NO₂, SO₂, and CO occurred in December because of increased local emissions and severe weather conditions, while the lowest values occurred in July mainly due to larger precipitation. The maximum O₃ concentrations occurred in warm seasons from May to August, which may be partly due to the high temperature and solar radiation. Diurnal analysis showed that hourly PM_2.5, PM₁₀, NO₂, and CO concentrations had two ascending stages accompanying by the two traffic peaks. However, the O₃ concentration variations were different with the highest concentration in the afternoon. A case study utilizing hybrid receptor models showed the significant impact of regional transport on the haze formation in Wuhan and revealed that the mainly potential polluted sources were located in the north and south of Wuhan, such as Baoding and Handan in Hebei province, and Changsha in Hunan province. Implications: Wuhan city requires a 5% reduction of the annual mean of PM_2.5 concentration by the end of 2017. In order to accomplish this goal, Wuhan has adopted some measures to improve its air quality. This work has determined the main pollution sources that affect the formation of haze in Wuhan by transport. We showed that apart from the local emissions, north and south of Wuhan were the potential sources contributing to the high PM_2.5 concentrations in Wuhan, such as Baoding and Handan in Hebei province, Zhumadian and Jiaozuo in Henan province, and Changsha and Zhuzhou in Hunan province.     

Predicting residential indoor concentrations of nitrogen dioxide,fine particulate matter,and elemental carbon using questionnaire and geographic information system based data

Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system, and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households.As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3–4 day samples of nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) in 43 low SES residences across multiple seasons from 2003 to 2005. Elemental carbon (EC) concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally located ambient monitor.The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM_2.5 was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO₂ with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50 m buffer of a home and distance from a truck route as important contributors to indoor levels of NO₂ and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and focused questionnaire data we can identify important predictors of indoor concentrations for multiple air pollutants in a high-risk population.     

Examining intra-urban variation in fine particle mass constituents using GIS and constrained factor analysis

Recent studies have used land use regression (LUR) techniques to explain spatial variability in exposures to PM_2.5 and traffic-related pollutants. Factor analysis has been used to determine source contributions to measured concentrations. Few studies have combined these methods, however, to construct and explain latent source effects. In this study, we derive latent source factors using confirmatory factor analysis constrained to non-negative loadings, and develop LUR models to predict the influence of outdoor sources on latent source factors using GIS-based measures of traffic and other local sources, central site monitoring data, and meteorology. We collected 3–4 day samples of nitrogen dioxide (NO₂) and PM_2.5 outside of 44 homes in summer and winter, from 2003 to 2005 in and around Boston, Massachusetts. Reflectance analysis, X-ray fluorescence spectroscopy (XRF), and high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) were performed on particle filters to estimate elemental carbon (EC), trace element, and water-soluble metals concentrations. Within our constrained factor analysis, a five-factor model was optimal, balancing statistical robustness and physical interpretability. This model produced loadings indicating long-range transport, brake wear/traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust. LUR models largely corroborated factor interpretations through covariate significance. For example, ‘long-range transport’ was predicted by central site PM_2.5 and season; ‘brake wear/traffic exhaust’ and ‘resuspended road dust’ by traffic and residential density; ‘diesel exhaust’ by percent diesel traffic on nearest major road; and ‘fuel oil combustion’ by population density. Results suggest that outdoor residential PM_2.5 source contributions can be partially predicted using GIS-based terms, and that LUR techniques can support factor interpretation for source apportionment. Together, LUR and factor analysis facilitate source identification, assessment of spatial and temporal variability, and more refined source exposure assignment for evaluation of source contributions to health outcomes in epidemiological studies.     

An inexpensive method to estimate CO and NOx emissions from mobile sources

CO and NO_x measurements from mobile sources at two urban locations in Córdoba City, Argentina, were used to develop a very simple method to estimate emission from these sources. This development was possible because primary urban air pollution in Córdoba comes mostly from mobile sources and because a field measurement campaign was conducted by the city government during 1995–1996 that has allowed us to have a complete and valuable data bank. Air concentrations of CO, NO_x as well as physical, and meteorological variables were measured at two urban sites with two monitoring stations. We compared the measured CO and NO_x air concentration data with the predictions of a method that uses regression analysis to estimate the emission factor from the mobile sources. The agreement is good, considering the simplicity of the approach.     

A land use regression model for predicting ambient volatile organic compound concentrations in Toronto,Canada

More than 25 studies have employed land use regression (LUR) models to estimate nitrogen oxides and to a lesser extent particulate matter indicators, but these methods have been less commonly applied to ambient concentrations of volatile organic compounds (VOCs). Some VOCs have high plausibility as sources of health effects and others are specific indicators of motor vehicle exhaust. We used LUR models to estimate spatial variability of VOCs in Toronto, Canada. Benzene, n-hexane and total hydrocarbons (THC) were measured from July 25 to August 9, 2006 at 50 locations using the TraceAir organic vapor monitors. Nitrogen dioxide (NO₂) was also sampled to assess its spatial pattern agreement with VOC exposures. Buffers for land use, population density, traffic density, physical geography, and remote sensing measures of greenness and surface brightness were also tested. The remote sensing measures have the highest correlations with VOCs and NO₂ levels (i.e., explains >36% of the variance). Our regression models explain 66–68% of the variance in the spatial distribution of VOCs, compared to 81% for the NO₂ model. The ranks of agreement between various VOCs range from 48 to 63% and increases substantially – up to 75% – for the top and bottom quartile groups. Agreements between NO₂ and VOCs are much smaller with an average rank of 36%. Future epidemiologic studies may therefore benefit from using VOCs as potential toxic agents for traffic-related pollutants.     

Behaviour and variability of local and regional oxidant levels (OX = O3 + NO2) measured in a polluted area in central-southern of Iberian Peninsula

The purpose of this work is to contribute to the understanding of the photochemical air pollution in central-southern of the Iberian Peninsula, analysing the behaviour and variability of oxidant levels (OX?=?O₃?+?NO₂), measured in a polluted area with the highest concentration of heavy industry in central Spain. A detailed air pollution database was observed from two monitoring stations. The data period used was 2008 and 2009, around 210,000 data, selected for its pollution and meteorological statistics, which are very representative of the region. Data were collected every 15 min, however hourly values were used to analyse the seasonal and daily ozone, NO, NO₂ and OX cycles. The variation of OX concentrations with NO _x is investigated, for the first time, in the centre of the Iberian Peninsula. The concentration of OX was calculated using the sum of a NO _x -independent ‘regional’ contribution (i.e. the O₃ background), and a linearly NO _x -dependent ‘local’ contribution. Monthly dependence of regional and local OX concentration was observed to determine when the maximum values may be expected. The variation of OX concentrations with levels of NO _x was also measured, in order to pinpoint the atmospheric sources of OX in the polluted areas. The ratios [NO₂]/[OX] and [NO₂]/[NO _x ] vs. [NO _x ] were analysed to find the fraction of OX in the form of NO₂, and the possible source of the local NO _x -dependent contribution, respectively. The progressive increase of the ratio [NO₂]/[OX] with [NO _x ] observed shows a greater proportion of OX in the form of NO₂ as the level of NO _x increases. The higher measured values in the ratio [NO₂]/[NO _x ] should not be attributed to NO _x emissions by vehicles; they could be explained by industrial emission, termolecular reactions or formaldehyde and HONO directly emitted by vehicles exhausts. We also estimate the rate of NO₂ photolysis, J _NO2?=?0.18–0.64 min^?1, a key atmospheric reaction that influence O₃ production and then the regional air quality. The first surface plot study of annual variation of the daily mean oxidant levels, obtained for this polluted area may be used to improve the atmospheric photochemical dynamic in this region of the Iberian Peninsula where there are undeniable air quality problems.     

Retrospective assessment of air quality management practices in Taiwan

In 1995, Taiwan's Environmental Protection Administration (EPA/TW) instituted a policy of levying emission taxes on polluters in order to combat the rampant national issue of pollution. Since that time, pollution control strategies, tightening exhaust emission standards for industry, improvements in fuel quality, and new stricter vehicle emission standards, etc., have been implemented. This study evaluates the effectiveness of these measures and examines the improvement of Taiwan's air quality. In this paper, we conduct a detailed analysis of change in the concentrations of pollutants (SO₂, NO_x and particulate matter [PM]) between two three-year periods (from 1996 to1998 and from 2000 to 2002). The pollution levels were generally lower in the latter period. Concentrations at 14 EPA/TW stations in central Taiwan were simulated and source apportionment analyses in three of Central Taiwan's largest cities were conducted using a trajectory transfer-coefficient air quality model. Correlation coefficients (r) between simulations and observations for the monthly means of the concentrations of SO₂, NO_x, PM_2.5 and PM₁₀ during the study periods at the 14 stations are 0.56, 0.63, 0.70 and 0.31, respectively. The sulfur control policy greatly reduced SO₂ concentration island-wide, a stringent emission standard put into place for gasoline vehicles reduced NO_x concentration along highways, and an emissions tax placed on construction sites, as well as a regular program for road-dust sweeping, reduced primary particulate matter. Among all of the pollution abatement policies implemented, the most effective method for reducing PM_2.5 concentrations in the three largest cities involved the reduction of fine ammonium sulfate aerosols from point sources (56–63% of net PM_2.5 reduction). The next largest reduction was attributed to a diminishment in primary PM_2.5 emanating from point sources (27–56% of net PM_2.5 reduction). Secondary particulate matter, especially sulfate, was reduced from distances up to 150 km leeward of major pollution point sources such as Taichung Power Plant.     

Process analysis of a regional air pollution episode over Pearl River Delta Region,China, using the MM5-CMAQ model

This study focuses on the influences of a warm high-pressure meteorological system on aerosol pollutants, employing the simulations by the Models-3/CMAQ system and the observations collected during October 10–12, 2004, over the Pearl River Delta (PRD) region. The results show that the spatial distributions of air pollutants are generally circular near Guangzhou and Foshan, which are cities with high emissions rates. The primary pollutant is particulate matter (PM) over the PRD. MM5 shows reasonable performance for major meteorological variables (i.e., temperature, relative humidity, wind direction) with normalized mean biases (NMB) of 4.5–38.8% and for their time series. CMAQ can capture one peak of all air pollutant concentrations on October 11, but misses other peaks. The CMAQ model systematically underpredicts the mass concentrations of all air pollutants. Compared with chemical observations, SO₂ and O₃ are predicted well with a correlation coefficient of 0.70 and 0.65. PM_2.5 and NO are significantly underpredicted with an NMB of 43% and 90%, respectively. The process analysis results show that the emission, dry deposition, horizontal transport, and vertical transport are four main processes affecting air pollutants. The contributions of each physical process are different for the various pollutants. The most important process for PM₁₀ is dry deposition, and for NO_x it is transport. The contributions of horizontal and vertical transport processes vary during the period, but these two processes mostly contribute to the removal of air pollutants at Guangzhou city, whose emissions are high. For this high-pressure case, the contributions of the various processes show high correlations in cities with the similar geographical attributes. According to the statistical results, cities in the PRD region are divided into four groups with different features. The contributions from local and nonlocal emission sources are discussed in different groups.

Implications: The characteristics of aerosol pollution episodes are intensively studied in this work using the high-resolution modeling system MM5/SMOKE/CMAQ, with special efforts on examining the contributions of different physical and chemical processes to air concentrations for each city over the PRD region by a process analysis method, so as to provide a scientific basis for understanding the formation mechanism of regional aerosol pollution under the high-pressure system over PRD.