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.     

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.     

Assessing the spatial distribution of nitrogen dioxide in London,Ontario

Land use regression (LUR) models have been widely used to characterize the spatial distribution of urban air pollution and estimate exposure in epidemiologic studies. However, spatial patterns of air pollution vary greatly between cities due to local source type and distribution. London, Ontario, Canada, is a medium-sized city with relatively few and isolated industrial point sources, which allowed the study to focus on the contribution of different transportation sectors to urban air pollution. This study used LUR models to estimate the spatial distribution of nitrogen dioxide (NO₂) and to identify local sources influencing NO₂ concentrations in London, ON. Passive air sampling was conducted at 50 locations throughout London over a 2-week period in May–June 2010. NO₂ concentrations at the monitored locations ranged from 2.8 to 8.9 ppb, with a median of 5.2 ppb. Industrial land use, dwelling density, distance to highway, traffic density, and length of railways were significant predictors of NO₂ concentrations in the final LUR model, which explained 78% of NO₂ variability in London. Traffic and dwelling density explained most of the variation in NO₂ concentrations, which is consistent with LUR models developed in other Canadian cities. We also observed the importance of local characteristics. Specifically, 17% of the variation was explained by distance to highways, which included the impacts of heavily traveled corridors transecting the southern periphery of the city. Two large railway yards and railway lines throughout central areas of the city explained 9% of NO₂ variability. These results confirm the importance of traditional LUR variables and highlight the importance of including a broader array of local sources in LUR modeling. Finally, future analyses will use the model developed in this study to investigate the association between ambient air pollution and cardiovascular disease outcomes, including plaque burden, cholesterol, and hypertension.

Implications: Monitoring and modeling of NO₂ throughout the city of London represents an important step toward assessing air pollution health effects in a mid-sized Canadian city. The study supports the introduction of railways to LUR modeling of NO₂. Railways explained approximately 9% of the variability in ambient NO₂ concentrations in London, which suggests that local sources captured by land-use indicators may contribute to the efficacy of LUR models. These findings provide insights relevant to other medium and smaller sized cities with similar land use and transportation infrastructure. Furthermore, London is a central hub for medical research and treatment in southwestern Ontario, with facilities such as the Robarts Research Institute, London Regional Cancer Program (LRCP), and Stroke Prevention & Atherosclerosis Research Centre (SPARC). The models developed in this study will provide estimates of exposure for future analyses examining air pollution health effects in this data-rich population.     

Predicting regional space–time variation of PM2.5 with land-use regression model and MODIS data

Purpose  

Existing land-use regression (LUR) models use land use/cover, population, and traffic information to predict long-term intra-urban variation of air pollution. These models are limited to explaining spatial variation of air pollutants, and few of them are capable of addressing temporal variability. This article proposes a space–time LUR model at a regional scale by incorporating aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS).     

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.     

Statistical and diagnostic evaluation of a new-generation urban dispersion modelling system against an extensive dataset in the Helsinki area

We have developed a modelling system for predicting the traffic volumes, emissions from stationary and vehicular sources, and atmospheric dispersion of pollution in an urban area. This paper describes a comparison of the NO_x and NO₂ concentrations predicted using this modelling system with the results of an urban air quality monitoring network. We performed a statistical analysis to determine the agreement between predicted and measured hourly time series of concentrations at four permanently located and three mobile monitoring stations in the Helsinki Metropolitan Area in 1996–1997 (at a total of ten urban and suburban measurement locations). At the stations considered, the so-called index of agreement values of the predicted and measured time series of the NO₂ concentrations vary between 0.65 and 0.82, while the fractional bias values range from −0.29 to +0.26. In comparison with corresponding results presented in the literature, the agreement between the measured and predicted datasets is good, as indicated by these statistical parameters. The seasonal variations of the NO₂ concentrations were analysed in terms of the relevant meteorological parameters. We also analysed the difference between model predictions and measured data diagnostically, in terms of meteorological parameters, including wind speed and direction (the latter separately for two wind speed classes), atmospheric stability and ambient temperature, at two monitoring stations in central Helsinki. The modelling system tends to overpredict the measured NO₂ concentrations both at the highest (u⩾6 m s⁻¹) and at the lowest wind speeds (u<2 m s⁻¹). For higher wind speeds, the modelling system overpredicts the measured NO₂ concentrations in certain wind direction intervals; specific ranges were found for both monitoring stations considered. The modelling system tends to underpredict the measured concentrations in convective atmospheric conditions, and overpredict in stable conditions. The possible physico-chemical reasons for these differences are discussed.     

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.     

Application of CALINE4 to roadside NO/NO2 transformations

The CALINE4 roadway dispersion model has been applied to concentrations of NO_x and NO₂ measured near Gandy Boulevard in Tampa, FL (USA) during May 2002. A NO_x emission factor of 0.86 gr mi⁻¹ was estimated by treating NO+NO₂ (NO_x) as a conserved species and minimizing the differences between measured and calculated NO_x concentrations. This emission factor was then used to calculate NO₂ concentrations using the NO/NO₂ transformation reactions built into CALINE4. A comparison of measured and calculated NO₂ concentrations indicates that for ambient O₃ concentrations less than 40 ppb the model under-predicts the chemical transformation of NO. The enhanced transformation of NO may be due to reactions of NO with oxidants such as peroxy radicals that are present either in the atmosphere or in vehicle exhaust.     

Measurements of primary trace gases and NOY composition in Houston,Texas

Concentrations of CO, SO₂, NO, NO₂, and NO_Y were measured atop the University of Houston's Moody Tower supersite during the 2006 TexAQS-II Radical and Aerosol Measurement Project (TRAMP). The lowest concentrations of all primary and secondary species were observed in clean marine air in southerly flow. SO₂ concentrations were usually low, but increased dramatically in sporadic midday plumes advected from sources in the Houston Ship Channel (HSC), located NE of the site. Concentrations of CO and NO_x displayed large diurnal variations in keeping with their co-emission by mobile sources in the Houston Metropolitan Area (HMA). CO/NO_x emission ratios of 5.81 ± 0.94 were observed in the morning rush hour. Nighttime concentrations of NO_x (NO_x = NO + NO₂) and NO_Y (NO_Y = NO + NO₂ + NO₃ + HNO₃ + HONO + 21N₂O₅ + HO₂NO₂ + PANs + RONO₂ + p-NO₃^? + …) were highest in winds from the NNW-NE due to emission from mobile sources. Median ratios of NO_x/NO_Y were approximately 0.9 overnight, reflecting the persistence and/or generation of NO_Z (NO_Z = NO_Y ? NO_x) species in the nighttime Houston boundary layer, and approached unity in the morning rush hour. Daytime concentrations of NO_x and NO_Y were highest in winds from the HSC. NO_x/NO_Y ratios reached their minimum values (median ca 0.63) from 1300 to 1500 CST, near local solar noon, and air masses often retained enough NO_x to sustain additional O₃ formation farther downwind. HNO₃ and PANs comprised the dominant NO_Z species in the HMA, and on a median basis represented 17–20% and 12–15% of NO_Y, respectively, at midday. Concentrations of HNO₃, PANs, and NO_Z, and fractional contributions of these species to NO_Y, were at a maximum in NE flow, reflecting the source strength and reactivity of precursor emissions in the HSC. As a result, daytime O₃ concentrations were highest in air masses with HSC influence. Overall, our findings confirm the impact of the HSC as a dominant source region within the HMA. A comparison of total NO_Y measurements with the sum of measured NO_Y species (NO_Yi = NO_x + HNO₃ + PANs + HONO + p-NO₃^?) yielded excellent overall agreement during both day ([NO_Y](ppb) = ([NO_Yi](ppb)11.03 ± 0.16) ? 0.42; r² = 0.9933) and night ([NO_Y](ppb) = ([NO_Yi](ppb)11.01 ± 0.16) + 0.18; r² = 0.9975). A similar comparison between NO_Y–NO_x concentrations and the sum of NO_Zi (NO_Zi = HNO₃ + PANs + HONO + p-NO₃^?) yielded good overall agreement during the day ([NO_Z](ppb) = ([NO_Zi](ppb)11.01 ± 0.30) + 0.044 ppb; r² = 0.8527) and at night ([NO_Z](ppb) = ([NO_Zi](ppb)11.12 ± 0.69) + 0.16 ppb; r² = 0.6899). Median ratios of NO_Z/NO_Zi were near unity during daylight hours but increased to approximately 1.2 overnight, a difference of 0.15–0.50 ppb. Differences between NO_Z and NO_Zi rarely exceeded combined measurement uncertainties, and variations in NO_Z/NO_Zi ratios may have resulted solely from errors in conversion efficiencies of NO_Y species and changes in NO_Y composition. However, nighttime NO_Z/NO_Zi ratios and the magnitude of NO_Z ? NO_Zi differences were generally consistent with recent observations of ClNO₂ in the nocturnal Houston boundary layer.     

Analysis of radon origin by backward atmospheric transport modelling

This work shows how ambient radon concentrations measured at Cabauw station in central Netherlands are influenced by transport from different regions under typical transport conditions occurring during April and November, 2007 by means of atmospheric Lagrangian particle dispersion modelling in a receptor-oriented approach. Four specific regions have been isolated to assess their contribution to the modelled radon ambient concentrations at Cabauw, and two different radon flux assumptions. Westerly flows coming from the ocean are poor in radon and do not increase radon air concentrations unless there is some fetch over the British Isles. Continental transport, mainly from eastern and southern Europe, significantly increases radon background concentrations, reaching increments of 3 Bq m^?3. A constant 0.66 atoms cm^?2 s^?1 radon flux over land and zero over water bodies is a good approximation for the source term in order to study regional contributions and modulation of the radon background.     

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.     

Modeling reactive pollutant dispersion in an urban street canyon

Reactive pollutant dispersion in an urban street canyon with a street aspect ratio of one is numerically investigated using a computational fluid dynamics (CFD) model. The CFD model developed is a Reynolds-averaged Navier–Stokes equations (RANS) model with the renormalization group (RNG) k–ε turbulence model and includes transport equations for NO, NO₂, and O₃ with simple photochemistry. An area emission source of NO and NO₂ is considered in the presence of background O₃ and street bottom heating (ΔT=5 °C) with an ambient wind perpendicular to the along-canyon direction. A primary vortex is formed in the street canyon and the line connecting the centers of cross-sectional vortices meanders over time and in the canyon space. The cross-canyon-averaged temperature and reactive pollutant concentrations oscillate with a period of about 15 min. The averaged temperature is found to be in phase with NO and NO₂ concentrations but out of phase with O₃ concentration. The photostationary state defect is small in the street canyon except for near the roof level and the upper downwind region of the canyon and its local minimum is observed near the center of the primary vortex. The budget analysis of NO (NO₂) concentration shows that the magnitude of the advection or turbulent diffusion term is much larger (larger) than that of the chemical reaction term and that the advection term is largely balanced by the turbulent diffusion term. On the other hand, the budget analysis of O₃ concentration shows that the magnitude of the chemical reaction term is comparable to that of the advection or turbulent diffusion term. The inhomogeneous temperature distribution itself affects O₃ concentration to some extent due to the temperature-dependent photolysis rate and reaction rate constant.     

Analysis of air quality within a street canyon using statistical and dispersion modelling techniques

The dispersion model, ADMS-Urban, alongside the statistical modelling technique, generalized additive modelling, have been used to predict hourly NO_x and nitrogen dioxide (NO₂) concentrations at a busy street canyon location and the results compared with measurements. Generalized additive models (GAMs) were constructed for NO₂ and NO_x concentrations using input data required to run ADMS-Urban. Bivariate polar plots have been produced from the wind flow (speed and direction) and pollution data (measured and predicted concentrations) to provide further information regarding the complex wind-pollutant interactions in an urban street canyon. The predictions made with the GAMs show excellent agreement with measured concentrations at this location, reproducing both the magnitude of NO_x and NO₂ concentrations and also the wind speed-wind direction dependence of pollutant sources within the canyon. However, the predictions made with ADMS-Urban under-estimated the measured NO_x by 11% and NO₂ by 21% and there are clear differences in the bivariate polar plots. Several sensitivity tests were carried out with ADMS-Urban in an attempt to produce predictions in closer agreement to those measured at Gillygate. Increasing the primary NO₂ fraction in ADMS-Urban (from 10% to 20%) had a considerable effect on the predictions made with this model, increasing NO₂ predictions by ∼20%. However, the bivariate plots still showed major differences to those of the measurements. This work illustrates that generalized additive modelling is a useful tool for investigating complex wind-pollutant interactions within a street canyon.     

Evaluation of the CAR-FMI model against measurements near a major road

A field measurement campaign was conducted near a major road in southern Finland from September 15 to October 30, 1995. The concentrations of NO, NO₂ and O₃ were measured simultaneously at three locations, at three heights (3.5, 6 and 10 m) on both sides of the road. Traffic densities and relevant meteorological parameters were also measured on-site. We have compared measured concentration data with the predictions of the road network dispersion model CAR-FMI, used in combination with a meteorological pre-processing model MPP-FMI. In comparison with corresponding results presented previously in the literature, the agreement of measured and predicted datasets was good, as measured using various statistical parameters. For all data (N=587), the index of agreement (IA) was 0.83, 0.82 and 0.89 for the measurements of NO_x, NO₂ and O₃, respectively. The IA is a statistical measure of the correlation of the predicted and measured time series of concentrations. However, the modelling system overpredicts NO_x concentrations with a fractional bias FB=+13%, and O₃ concentrations with FB=+8%, while for NO₂ concentrations FB=−2%. We also analyzed the difference between model predictions and measured data in terms of meteorological parameters. Model performance clearly deteriorated as the wind direction approached a direction parallel to the road, and for the lowest wind speeds. The range of variability concerning atmospheric stability, ambient temperature and the amount of solar radiation was modest during the measurement campaign. As expected, no clear dependencies of model performance were therefore detected in terms of these parameters. The experimental dataset is available for the evaluation of other roadside dispersion models.     

Laser induced fluorescence instrument for NO2 measurements: Observations at a central Italy background site

A laser induced fluorescence (LIF) instrument has been developed to measure tropospheric NO₂ with low detection limit. The instrument design, development and first measurements are reported. There are also details of the temporal gate system built for the fluorescence acquisition. The instrument is able to make fast measurements (up to 4 Hz) and shows a limit of detection of 10 pptv/60 s. Continuous observations (2 weeks in summer 2007) in a small town in central Italy were used to test the performance of the instrument and to study the photochemistry of ozone in a background site. LIF and a commercial chemiluminescence (CL) instrument simultaneous observations of NO₂ show a good linearity (LIF = 1.02 CL + 0.6 (ppb), R² = 0.98) but there is a bias of the commercial instrument of about 0.60 ppbv on average. The overestimation of the CL system is probably due to conversion of NO_y species into NO by the molybdenum converter used in the CL instrument to detect NO₂. Analysis of 1 s data is used to test the instrument response and the coupling between nitrogen oxides and ozone.     

A full year evaluation of the CALIOPE-EU air quality modeling system over Europe for 2004

The CALIOPE-EU high-resolution air quality modeling system, namely WRF-ARW/HERMES-EMEP/CMAQ/BSC-DREAM8b, is developed and applied to Europe (12 km × 12 km, 1 h). The model performances are tested in terms of air quality levels and dynamics reproducibility on a yearly basis. The present work describes a quantitative evaluation of gas phase species (O₃, NO₂ and SO₂) and particulate matter (PM2.5 and PM10) against ground-based measurements from the EMEP (European Monitoring and Evaluation Programme) network for the year 2004. The evaluation is based on statistics. Simulated O₃ achieves satisfactory performances for both daily mean and daily maximum concentrations, especially in summer, with annual mean correlations of 0.66 and 0.69, respectively. Mean normalized errors are comprised within the recommendations proposed by the United States Environmental Protection Agency (US-EPA). The general trends and daily variations of primary pollutants (NO₂ and SO₂) are satisfactory. Daily mean concentrations of NO₂ correlate well with observations (annual correlation r = 0.67) but tend to be underestimated. For SO₂, mean concentrations are well simulated (mean bias = 0.5 μg m^?3) with relatively high annual mean correlation (r = 0.60), although peaks are generally overestimated. The dynamics of PM2.5 and PM10 is well reproduced (0.49 < r < 0.62), but mean concentrations remain systematically underestimated. Deficiencies in particulate matter source characterization are discussed. Also, the spatially distributed statistics and the general patterns for each pollutant over Europe are examined. The model performances are compared with other European studies. While O₃ statistics generally remain lower than those obtained by the other considered studies, statistics for NO₂, SO₂, PM2.5 and PM10 present higher scores than most models.     

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.     

Aircraft measurements of O3, NOx,CO, VOCs,and SO2 in the Yangtze River Delta region

In this study, air pollutants, including ozone (O₃), nitrogen oxides (NO_x = NO + NO₂), carbon monoxides (CO), sulfur dioxide (SO₂), and volatile organic compounds (VOCs) measured in the Yangtze River Delta (YRD) region during several air flights between September/30 and October/11 are analyzed. This measurement provides horizontal and vertical distributions of air pollutants in the YRD region. The analysis of the result shows that the measured O₃ concentrations range from 20 to 60 ppbv. These values are generally below the US national standard (84 ppbv), suggesting that at the present, the O₃ pollutions are modest in this region. The NO_x concentrations have strong spatial and temporal variations, ranging from 3 to 40 ppbv. The SO₂ concentrations also have large spatial and temporal variations, ranging from 1 to 35 ppbv. The high concentrations of CO are measured with small variations, ranging from 3 to 7 ppmv. The concentrations of VOCs are relatively low, with the total VOC concentrations of less than 6 ppbv. The relative small VOC concentrations and the relative large NO_x concentrations suggest that the O₃ chemical formation is under a strong VOC-limited regime in the YRD region. The measured O₃ and NO_x concentrations are strongly anti-correlated, indicating that enhancement in NO_x concentrations leads to decrease in O₃ concentrations. Moreover, the O₃ concentrations are more sensitive to NO_x concentrations in the rural region than in the city region. The ratios of Δ[O₃]/Δ[NO_x] are ?2.3 and ?0.25 in the rural and in the city region, respectively. In addition, the measured NO_x and SO₂ concentrations are strongly correlated, highlighting that the NO_x and SO₂ are probably originated from same emission sources. Because SO₂ emissions are significantly originated from coal burnings, the strong correlation between SO₂ and NO_x concentrations suggests that the NO_x emission sources are mostly from coal burned sources. As a result, the future automobile increases could lead to rapid enhancements in O₃ concentrations in the YRD region.     

PM2.5, soot and NO2 indoor–outdoor relationships at homes,pre-schools and schools in Stockholm,Sweden

In developed nations people spend about 90% of their time indoors. The relationship between indoor and outdoor air pollution levels is important for the understanding of the health effects of outdoor air pollution. Although other studies describe both the outdoor and indoor atmospheric environment, few excluded a priori major indoor sources, measured the air exchange rate, included more than one micro-environment and included the presence of human activity. PM_2.5, soot, NO₂ and the air exchange rate were measured during winter and summer indoors and outdoors at 18 homes (mostly apartments) of 18 children (6–11-years-old) and also at the six schools and 10 pre-schools that the children attended. The three types of indoor environments were free of environmental tobacco smoke and gas appliances, as the aim was to asses to what extent PM_2.5, soot and NO₂ infiltrate from outdoors to indoors. The median indoor and outdoor PM_2.5 levels were 8.4 μg m^?3 and 9.3 μg m^?3, respectively. The median indoor levels for soot and NO₂ were 0.66 m^?1 × 10^?5 and 10.0 μg m^?3, respectively. The respective outdoor levels were 0.96 m^?1 × 10^?5 and 12.4 μg m^?3. The median indoor/outdoor (I/O) ratios were 0.93, 0.76 and 0.92 for PM_2.5, soot and NO₂, respectively. Their infiltration factors were influenced by the micro-environment, ventilation type and air exchange rate, with aggregated values of 0.25, 0.55 and 0.64, respectively. Indoor and outdoor NO₂ levels were strongly associated (R² = 0.71), followed by soot (R² = 0.50) and PM_2.5 (R² = 0.16). In Stockholm, the three major indoor environments occupied by children offer little protection against combustion-related particles and gases in the outdoor air. Outdoor PM_2.5 seems to infiltrate less, but indoor sources compensate.