Spatial Variability Of Air Pollutants Research Articles

The Role of Neighborhood Air Pollution in Disparate Racial and Ethnic Asthma Acute Care Use.

The share of Black or Latinx residents in a census tract remains associated with asthma-related Emergency Department visit rates after controlling for socioeconomic factors. The extent to which evident disparities relate to within-city heterogeneity of long-term air pollution exposure remains unclear. To investigate the role of intraurban spatial variability of air pollution in asthma acute care use disparity. An administrative database was used to define census tract population-based incidence rates of asthma-related Emergency Department visits. We estimate the association between census tract incidence rates and (a) average fine and coarse particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2); and (b) racial/ethnic composition using generalized linear models controlling for socioeconomic and housing covariates. We additionally examine for attenuation of incidence risk ratios (IRR) associated with race/ethnicity when controlling for air pollution exposure. PM2.5, PM10, and SO2 are each associated with census tract-level incidence rates of asthma-related ED visits and multipollutant models show evidence of independent risk associated with PM10 and SO2. Association between census tract incidence rates and Black resident share (IRR [CI] = 1.51 [1.48-1.54]) is attenuated by 24% when accounting for air pollution (1.39 [1.35-1.42]), and the association with Latinx resident share (1.11 [1.09-1.13]) is attenuated by 32% (1.08 [1.06-1.10]). Neighborhood-level rates of asthma acute care use are associated with local air pollution. Controlling for air pollution attenuates associations with census tract racial/ethnic composition, suggesting that intracity variability in air pollution could contribute to neighborhood-to-neighborhood asthma morbidity disparities.

Determinants of spatial variability of air pollutant concentrations in a street canyon network measured using a mobile laboratory and a drone

Urban air pollutant concentrations are highly variable both in space and time. In order to understand these variabilities high-resolution measurements of air pollutants are needed. Here we present results of a mobile laboratory and a drone measurements made within a street-canyon network in Helsinki, Finland, in summer and winter 2017. The mobile laboratory measured the total number concentration (N) and lung-deposited surface area (LDSA) of aerosol particles, and the concentrations of black carbon, nitric oxide (NOx) and ozone (O3). The drone measured the vertical profile of LDSA. The main aims were to examine the spatial variability of air pollutants in a wide street canyon and its immediate surroundings, and find the controlling environmental variables for the observed variability's.The highest concentrations with the most temporal variability were measured at the main street canyon when the mobile laboratory was moving with the traffic fleet for all air pollutants except O3. The street canyon concentration levels were more affected by traffic rates whereas on surrounding areas, meteorological conditions dominated. Both the mean flow and turbulence were important, the latter particularly for smaller aerosol particles through LDSA and N. The formation of concentration hotspots in the street network were mostly controlled by mechanical processes but in winter thermal processes became also important for aerosol particles. LDSA showed large variability in the profile shape, and surface and background concentrations. The expected exponential decay functions worked better in well-mixed conditions in summer compared to winter. We derived equation for the vertical decay which was mostly controlled by the air temperature. Mean wind dominated the profile shape over both thermal and mechanical turbulence. This study is among the first experimental studies to demonstrate the importance of high-resolution measurements in understanding urban pollutant variability in detail.

Spatial variability of air pollutants in a megacity characterized by mobile measurements

Abstract. Characterization of the spatial distributions of air pollutants on an intracity scale is important for understanding local sources, secondary formation, and human exposure. In this study, we conducted in situ mobile measurements for the chemical composition of fine particles, volatile organic compounds (VOCs), oxygenated VOCs (OVOCs), and common gas pollutants in winter in the megacity of Beijing. The spatial patterns of these pollutants under different pollution conditions were investigated. During the non-haze days all pollutants showed significant spatial variability. Large spatial variations of secondary species including OVOCs and secondary aerosol species highlight the chemical heterogeneity. In particular, the inorganic chemical composition of fine particles varied greatly on the 65 km urban highway, suggesting a wide range of particle neutralization in the megacity of Beijing. Localized sources such as vehicle, cooking, and industrial emissions led to hot spots and nonuniform distributions of primary pollutants in the city. The spatial heterogeneity of air pollutants under less polluted conditions calls for a future need of using fine-resolution models to evaluate human exposure and to develop pollution control strategies. During the haze day the spatial variabilities of secondary gaseous and particulate pollutants were largely reduced, explained by both the elevated urban background of the polluted air mass and the enhanced secondary formation by elevated precursor concentrations and heterogeneous or aqueous pathways. Although localized primary emissions were accumulated under stagnant haze conditions, the chemical composition of fine particles became relatively homogeneous because of the predominant secondary contributions. A uniform spatial pattern of particle neutralization was observed. The concentrations of hydrocarbons and less oxygenated OVOCs showed good positive spatiotemporal correlations during the haze day, while the concentrations of more oxygenated OVOCs showed good positive correlations among themselves but moderate negative correlations with the concentrations of hydrocarbons, less oxygenated OVOCs, and aerosol species. Our results indicate a spatial chemical homogeneity on the megacity scale to promote efficient secondary organic aerosol (SOA) production under haze conditions.

A new mobile monitoring approach to characterize community-scale air pollution patterns and identify local high pollution zones

Urban air pollution is quite complex and exhibits significant spatial variability within communities. Traditional centralized monitoring captures temporal variability as well as the long-term trends of air pollution very well, but mapping spatial variability of air pollution in communities at high resolution would require large number of air quality monitors distributed across the community. Mobile monitoring complements stationary monitoring approaches by measuring pollution levels on accessible road networks in urban communities. This paper presents the application of an integrated mobile measurement and data analysis approach to study community-level air pollution patterns, separate the regional background and local contributions, and identify high pollution zones for black carbon (BC), ultrafine particles (UFP), and fine particulate matter (PM2.5). This study was conducted during different periods from 2017 to 2019 in three California cities (Richmond, Stockton, and Commerce) with diverse community and source characteristics.The study found that traffic was the dominant primary source of air pollution in both urban and suburban settings. Urban areas adjacent to large area sources, such as ports and railyards had increased pollutant enhancements due to either direct or indirect emissions, while suburban areas containing unpaved roads were observed to have large PM2.5 enhancement due to the resuspended dust. The analysis disaggregated the contribution of regional and local sources to local air pollution and found that regional background sources contributed up to 75% of the PM2.5 concentrations, while local sources contributed more than half of BC and UFP. The study suggests that 15–30 repeated measurements may be sufficient to map the general air pollution patterns within the community, while some extreme high pollution zones can be identified with fewer repeats (5–10). These techniques can be used for initial screening of air pollution variability within the community, and help identify priority areas for conducting follow-up long-term air quality measurements. The techniques also informs the relative importance of regional and local actions to reduce community pollutant levels.

Air Quality over China

The strong economic growth in China in recent decades, together with meteorological factors, has resulted in serious air pollution problems, in particular over large industrialized areas with high population density. To reduce the concentrations of pollutants, air pollution control policies have been successfully implemented, resulting in the gradual decrease of air pollution in China during the last decade, as evidenced from both satellite and ground-based measurements. The aims of the Dragon 4 project “Air quality over China” were the determination of trends in the concentrations of aerosols and trace gases, quantification of emissions using a top-down approach and gain a better understanding of the sources, transport and underlying processes contributing to air pollution. This was achieved through (a) satellite observations of trace gases and aerosols to study the temporal and spatial variability of air pollutants; (b) derivation of trace gas emissions from satellite observations to study sources of air pollution and improve air quality modeling; and (c) study effects of haze on air quality. In these studies, the satellite observations are complemented with ground-based observations and modeling.

Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations

Air quality is one of the major issues within an urban area that affect people's living environment and health conditions. Existing observations are not adequate to provide a spatiotemporally comprehensive air quality information for vulnerable populations to plan ahead. Launched in 2017, TROPOspheric Monitoring Instrument (TROPOMI) provides a high spatial resolution (~5 km) tropospheric air quality measurement that captures the spatial variability of air pollution, but still limited by its daily overpass in the temporal dimension and relatively short historical records. Integrating with the hourly available AirNOW observations by ground-level discrete stations, we proposed and compared two deep learning methods that learn the relationship between the ground-level nitrogen dioxide (NO2) observation from AirNOW and the tropospheric NO2 column density from TROPOMI to downscale the daily NO2 to an hourly resolution. The input predictors include the locations of AirNOW stations, AirNOW NO2 observations, boundary layer height, other meteorological status, elevation, major roads, and power plants. The learned relationship can be used to produce NO2 emission estimates at the sub-urban scale on an hourly basis. The two methods include 1) an integrated method between inverse weighted distance and a feed forward neural network (IDW + DNN), and 2) a deep matrix network (DMN) that maps the discrete AirNOW observations directly to the distribution of TROPOMI observations. We further compared the accuracies of both models using different configurations of input predictors and validated their average Root Mean Squared Error (RMSE), average Mean Absolute Error (MAE) and the spatial distribution of errors. Results show that DMN generates more reliable NO2 estimates and captures a better spatial distribution of NO2 concentrations than the IDW + DNN model.

Temporal and spatial effect of air pollution on hospital admissions for myocardial infarction: a case-crossover study.

In studies showing associations between ambient air pollution and myocardial infarction (MI), data have been lacking on the inherent spatial variability of air pollution. The aim of this study was to determine whether the long-term spatial distribution of air pollution influences short-term temporal associations between air pollution and admission to hospital for MI. We identified adults living in Calgary who were admitted to hospital for an MI between 2004 and 2012. We evaluated associations between short-term exposure to air pollution (ozone [O3], nitrogen dioxide [NO2], sulfur dioxide [SO2], carbon monoxide [CO], particulate matter < 10 μm in diameter [PM10] and particulate matter < 2.5 μm in diameter [PM2.5]), and hospital admissions for MI using a time-stratified, case-crossover study design. Air Quality Health Index (AQHI) scores were calculated from a composition of O3, NO2 and PM2.5. Conditional logistic regression models were stratified by low, medium and high levels of neighbourhood NO2 concentrations derived from land use regression models; results of these analyses are presented as odds ratios (ORs) with 95% confidence intervals (CIs). From 2004 to 2012, 6142 MIs were recorded in Calgary. Individuals living in neighbourhoods with higher long-term air pollution concentrations were more likely to be admitted to hospital for MI after short-term elevations in air pollution (e.g., 5-day average NO2: OR 1.20, 95% CI 1.03-1.40, per interquartile range [IQR]) as compared with regions with lower air pollution (e.g., 5-day average NO2: OR 0.90, 95% CI 0.78-1.04, per IQR). In high NO2 tertiles, the AQHI score was associated with MI (e.g., 5-day average OR 1.13, 95% CI 1.02-1.24, per IQR; 3-day average OR 1.13, 95% CI 1.04-1.23, per IQR). Our results show that the effect of air pollution on hospital admissions for MI was stronger in areas with higher NO2 concentrations than that in areas with lower NO2 concentrations. Individuals living in neighbourhoods with higher traffic-related pollution should be advised of the health risks and be attentive to special air quality warnings.

Mapping Carbon Monoxide Pollution of Residential Areas in a Polish City

Road traffic is among the main sources of atmospheric pollution in cities. Maps of pollutants are based on geostatistical models using a digital model of the city along with traffic parameters allowing for ongoing analyses and prediction of the condition of the environment. The aim of the work was to determine the size of areas at risk of carbon monoxide pollution derived from road traffic along with determining the number of inhabitants exposed to excessive CO levels using geostatistical modeling on the example of the city of Bydgoszcz, a city in the northern part of Poland. The COPERT STREET LEVEL program was used to calculate CO emissions. Next, based on geostatistical modelling, a prediction map of CO pollution (kg/year) was generated, along with determining the level of CO concentration (mg/m3/year). The studies accounted for the variability of road sources as well as the spatial structure of the terrain. The results are presented for the city as well as divided into individual housing estates. The level of total carbon monoxide concentration for the city was 5.18 mg/m3/year, indicating good air quality. Detailed calculation analyses showed that the level of air pollution with CO varies in the individual housing estates, ranging from 0.08 to 35.70 mg/m3/year. Out of the 51 studied residential estates, the limit value was exceeded in 10, with 45% of the population at risk of poor air quality. The obtained results indicate that only detailed monitoring of the level of pollution can provide us with reliable information on air quality. The results also show in what way geostatistical tools can be used to map the spatial variability of air pollution in a city. The obtained spatial details can be used to improve estimated concentration based on interpolation between direct observation and prediction models.

A Nonlinear Land Use Regression Approach for Modelling NO2 Concentrations in Urban Areas—Using Data from Low-Cost Sensors and Diffusion Tubes

Land Use Regression (LUR) based on multiple linear regression model is one of the techniques used most frequently for modelling the spatial variability of air pollution and assessing exposure in urban areas. In this paper, a nonlinear generalised additive model is proposed for LUR and its performance is compared to a linear model in Sheffield, UK for the year 2019. Pollution models were estimated using NO2 measurements obtained from 188 diffusion tubes and 40 low-cost sensors. Performance of the models was assessed by calculating several statistical metrics including correlation coefficient (R) and root mean square error (RMSE). High resolution (100 m × 100 m) maps demonstrated higher levels of NO2 in the city centre, eastern side of the city and on major roads. The results showed that the nonlinear model outperformed the linear counterpart and that the model estimated using NO2 data from diffusion tubes outperformed the models using data from low-cost sensors or both low-cost sensors and diffusion tubes. The proposed method provides a basis for further application of advanced nonlinear modelling approaches to constructing LUR models in urban areas which enable quantifying small scale variability in pollution levels.

A spatial analysis of childhood cancer and industrial air pollution in a metropolitan area of Colombia.

Air pollutants are considered carcinogenic to humans. In some European countries, an association between industrial air pollution and childhood cancer has been established. This relationship has not been addressed in Latin America, despite the spatial variability of air pollutants that may limit the extrapolation of the results to other geographical areas. To conduct a spatial analysis of the relationship between childhood cancer and proximity to industrial sources of air pollution in a metropolitan area of Colombia. Incident cases of childhood cancers were obtained from the Population-based Cancer Registry of the Metropolitan Area of Bucaramanga during 2000-2015. Local and focused cluster tests were used for the detection of spatial clusters, and the Poisson multivariable model was used to evaluate the combined effects of spatial variables. The Kulldorff's focused test found a significant spatial cluster (P<0.001) around one industrial agglomerate and the multivariable model results suggests that the distance effect is modified by the directional effect of the wind. A spatial cluster of incident cases of childhood cancer occurred in the municipality of Bucaramanga, Colombia. Our finding supports the hypothesis that childhood cancer might be related with industrial air pollution exposure in a Latin American city.

Identifying air pollution source impacts in urban communities using mobile monitoring.

With increasing population, rapid urbanization, and increased migration to cities, the local impacts of increasing transportation and industrial-related air pollution are of growing concern worldwide. Elevated air pollution concentrations near these types of sources have been linked to adverse health effects including acute and chronic respiratory and cardiovascular diseases. Mobile monitoring has proven to be a useful technique to characterize spatial variability of air pollution in urban areas and pollution concentration gradients from specific sources. A study was conducted in the Kansas City, Kansas (USA) metropolitan area using mobile monitoring to characterize the spatial variability and gradients of air pollutants to identify the contribution of multiple sources on community-level air quality in a complex urban environment. Measurements focused on nitrogen dioxide (NO2), black carbon (BC), and ultrafine particulate matter (UFP). Mobile monitoring showed that median concentrations of these pollutants ranged by up to a factor of three between the communities, with individual measurements ranging over an order of magnitude within the community. Evaluating these air quality measurements with wind direction data highlighted the influence of specific and combinations of air pollution sources on these elevated concentrations, which can provide valuable information to environmental and public health officials in prioritizing and implementing cost-effect air quality management strategies to reduce exposures for urban populations.

Is a Land Use Regression Model Capable of Predicting the Cleanest Route to School?

Land Use Regression (LUR) modeling is a widely used technique to model the spatial variability of air pollutants in epidemiology. In this study, we explore whether a LUR model can predict home-to-school commuting exposure to black carbon (BC). During January and February 2019, 43 children walking to school were involved in a personal monitoring campaign measuring exposure to BC and tracking their home-to-school routes. At the same time, a previously developed LUR model for the study area was applied to estimate BC exposure on points along the route. Personal BC exposure varied widely with mean ± SD of 9003 ± 4864 ng/m3. The comparison between the two methods showed good agreement (Pearson’s r = 0.74, Lin’s Concordance Correlation Coefficient = 0.6), suggesting that LUR estimates are capable of catching differences among routes and predicting the cleanest route. However, the model tends to underestimate absolute concentrations by 29% on average. A LUR model can be useful in predicting personal exposure and can help urban planners in Milan to build a healthier city for schoolchildren by promoting less polluted home-to-school routes.

Spatial-temporal variation characteristics of air pollution in Henan of China: Localized emission inventory, WRF/Chem simulations and potential source contribution analysis

Henan is the most populous province and one of the most seriously polluted areas in China at present. In this study, we establish an integrated atmospheric emission inventory of primary air pollutants in Henan province for the target year of 2012. The inventory developed here accounts for detailed activity levels of 11 categories of primary anthropogenic emission sources, and determines the best available representation of emission factors. Further, we allocate the annual emissions into a high spatial resolution of 3km×3km with ArcGIS methodology and surrogate indices, such as regional population distribution and gross domestic product (GDP). Our results show that the emissions of VOCs, SO2, PM10, PM2.5, NOX, NH3, CO, BC and OC are about 1.15, 1.24, 1.29, 0.70, 1.93, 1.05, 7.92, 0.27 and 0.25milliontons, respectively. The majority of these pollutant emissions comes from the Central Plain Urban Agglomeration (CPUA) region, particularly Zhengzhou and Pingdingshan. By combining with the emission inventory with the WRF/Chem modeling and backward trajectory analysis, we investigate the temporal and spatial variability of air pollution in the province and explore the causes of higher pollutants concentrations in the region of CPUA during the heavily polluted period of January. The results demonstrate that intensive pollutants emissions and unfavorable meteorological conditions are the main causes of the heavy pollution. Besides, Weighted Potential Source Contribution Function (WPSCF) analysis indicates that local emissions remain the major contributor of PM2.5 in Henan province, although emissions from the neighboring provinces (e.g. Shanxi, Shaanxi, Anhui, and Shandong) are also important contributors.

Characterizing spatial variability of air pollution from vehicle traffic around the Houston Ship Channel area

Mobile emissions are a major source of urban air pollution and have been associated with a variety of adverse health outcomes. The Houston Ship Channel area is the home of a large number of diesel-powered vehicles emitting fine particulate matter (PM2.5; ≤2.5 μm in aerodynamic diameter) and nitrogen oxides (NOx). However, the spatial variability of traffic-related air pollutants in the Houston Ship Channel area has rarely been investigated. The objective of this study is to characterize spatial variability of PM2.5 and NOx concentrations attributable to on-road traffic in the Houston Ship Channel area in the year of 2011. We extracted the road network from the Texas Department of Transportation Road Inventory, and calculated emission rates using the Motor Vehicle Emission Simulator version 2014a (MOVES2014a). These parameters and preprocessed meteorological parameters were entered into a Research LINE-source Dispersion Model (RLINE) to conduct a simulation. Receptors were placed at 50 m resolution within 300 m to major roads and at 150 m resolution in the rest of the area. Our findings include that traffic-related PM2.5 were mainly emitted from trucks, while traffic-related NOx were emitted from both trucks and cars. The traffic contributed 0.90 μg/m3 PM2.5 and 29.23 μg/m3 NOx to the annual average mass concentrations of on-road air pollution, and the concentrations of the two pollutants decreased by nearly 40% within 500 m distance to major roads. The pollution level of traffic-related PM2.5 and NOx was higher in winter than those in the other three seasons. The Houston Ship Channel has earlier morning peak hours and relative late afternoon hours, which indicates the influence of goods movement from port activity. The varied near-road gradients illustrate that proximities to major roads are not an accurate surrogate of traffic-related air pollution.

Land use regression modelling of air pollution in high density high rise cities: A case study in Hong Kong

Land use regression (LUR) is a common method of predicting spatial variability of air pollution to estimate exposure. Nitrogen dioxide (NO2), nitric oxide (NO), fine particulate matter (PM2.5), and black carbon (BC) concentrations were measured during two sampling campaigns (April–May and November–January) in Hong Kong (a prototypical high-density high-rise city). Along with 365 potential geospatial predictor variables, these concentrations were used to build two-dimensional land use regression (LUR) models for the territory. Summary statistics for combined measurements over both campaigns were: a) NO2 (Mean=106μg/m3, SD=38.5, N=95), b) NO (M=147μg/m3, SD=88.9, N=40), c) PM2.5 (M=35μg/m3, SD=6.3, N=64), and BC (M=10.6μg/m3, SD=5.3, N=76). Final LUR models had the following statistics: a) NO2 (R2=0.46, RMSE=28μg/m3) b) NO (R2=0.50, RMSE=62μg/m3), c) PM2.5 (R2=0.59; RMSE=4μg/m3), and d) BC (R2=0.50, RMSE=4μg/m3). Traditional LUR predictors such as road length, car park density, and land use types were included in most models. The NO2 prediction surface values were highest in Kowloon and the northern region of Hong Kong Island (downtown Hong Kong). NO showed a similar pattern in the built-up region. Both PM2.5 and BC predictions exhibited a northwest-southeast gradient, with higher concentrations in the north (close to mainland China). For BC, the port was also an area of elevated predicted concentrations. The results matched with existing literature on spatial variation in concentrations of air pollutants and in relation to important emission sources in Hong Kong. The success of these models suggests LUR is appropriate in high-density, high-rise cities.

Back-extrapolating a land use regression model for estimating past exposures to traffic-related air pollution.

Land use regression (LUR) models rely on air pollutant measurements for their development, and are therefore limited to recent periods where such measurements are available. Here we propose an approach to overcome this gap and calculate LUR models several decades before measurements were available. We first developed a LUR model for NOx using annual averages of NOx at all available air quality monitoring sites in Israel between 1991 and 2011 with time as one of the independent variables. We then reconstructed historical spatial data (e.g., road network) from historical topographic maps to apply the model's prediction to each year from 1961 to 2011. The model's predictions were then validated against independent estimates about the national annual NOx emissions from on-road vehicles in a top-down approach. The model's cross validated R2 was 0.74, and the correlation between the model's annual averages and the national annual NOx emissions between 1965 and 2011 was 0.75. Information about the road network and population are persistent predictors in many LUR models. The use of available historical data about these predictors to resolve the spatial variability of air pollutants together with complementary national estimates on the change in pollution levels over time enable historical reconstruction of exposures.

Impact of Urbanization on Local Air Quality: Differences in Urban and Rural Areas of Balikesir, Turkey

AbstractOzone (O3) is a secondary pollutant which is produced photo‐chemically by reactions involving NOx and benzene, toluene, ethylbenzene, and m‐, p‐xylenes (BTEX) in the presence of sunlight. The present study determined outdoor air quality in both the urban area of Balikesir City and rural area (around Ikizcetepeler Dam) in the same region in the western part of Turkey. The main objectives of this study were to analyze the temporal evolution and qualify the spatial distribution by using geostatistical techniques; to determine the spatial variability of air pollutants using the spatial auto‐correlation statistic; and to identify the local spatial patterns of the pollutants in order to highlight the areas of potential risk of O3, NO2, and BTEX pollution and its possible causes. For this purpose firstly, the data were compiled by using passive sampling in winter and in summer. Concentrations of O3 ranged from 14.03 to 42.43 μ g/m3 in winter and from 81.79 to 70.39 μ g/m3 in summer in urban area. The mean NO2 and BTEX concentrations varied between sites, while mean winter concentrations were higher than the mean in summer. During winter seasons, the motorway, residential areas, urban traffic, and industrial sites showed high BTEX levels compared to the suburban and rural sites. This paper investigates the spatial mapping of temporal trends in air quality for both urban and rural areas for annual, summer and winter means. The global Moran's I result demonstrated that O3, NO2, and BTEX had significant positive global spatial correlations. In addition, the results of local spatial auto‐correlation analysis showed the locations of significant high–high spatial clusters and low–low spatial outliers.

Remote sensing of exposure to NO2: satellite versus ground based measurement in a large urban area

Background: Satellite remote sensing may be a powerful and useful tool for exploring spatial variability of air pollution within an urban area, particularly in countries or areas where direct measu...

Quantifying temporal trends of atmospheric pollutants in Makkah (1997–2012)

There is a high interest in quantifying temporal trends of air pollutant concentrations as they serve to assess the effects of emission control strategies. In this paper temporal trends (1997–2012) in air pollutant concentrations in the City of Makkah, near the Holy Mosque, have been assessed with the help of Theil–Sen approach and changepoint analysis, which are applicable to both normal and non-normal distributed data and are therefore preferred over classic parametric statistics. Trend analysis of nitrogen oxides (NOx), nitric oxide (NO), nitrogen dioxide (NO2), carbon monoxide (CO), sulphur dioxide (SO2), particulate matter with aerodynamic diameter of 10 micron or less (PM10) and ozone (O3) was performed, not only at mean concentrations but also at several selected quantiles (minimum, first quartile, median, third quartile and maximum). Trends were positive in SO2, NO2, PM10 and ozone and negative in CO and NO concentrations. NOx (negative) and NO2 (positive) trends were predominantly non-significant, however other pollutants showed mostly significant trends. Trends vary at various metrics suggesting different behaviour of air pollutants at various quantiles of the distributions. Changepoint analysis was performed, which provides further insight into the temporal trends and highlights the points when the trends have changed significantly. Potential factors responsible for the observed temporal trends are discussed. Further work on source apportionment of various emission sources and spatial variability of air pollutants is required to provide further insight into the causes behind the trends of air pollutants and help better manage air pollutant levels in Makkah and elsewhere in the country.

Nitrogen dioxide levels estimated from land use regression models several years apart and association with mortality in a large cohort study

BackgroundLand Use Regression models (LUR) are useful to estimate the spatial variability of air pollution in urban areas. Few studies have evaluated the stability of spatial contrasts in outdoor nitrogen dioxide (NO2) concentration over several years. We aimed to compare measured and estimated NO2 levels 12 years apart, the stability of the exposure estimates for members of a large cohort study, and the association of the exposure estimates with natural mortality within the cohort.MethodsWe measured NO2 at 67 locations in Rome in 1995/96 and 78 sites in 2007, over three one-week-long periods. To develop LUR models, several land-use and traffic variables were used. NO2 concentration at each residential address was estimated for a cohort of 684,000 adults. We used Cox regression to analyze the association between the two estimated exposures and mortality.ResultsThe mean NO2 measured concentrations were 45.4 μg/m3 (SD 6.9) in 1995/96 and 44.6 μg/m3 (SD 11.0) in 2007, respectively. The correlation of the two measurements was 0.79. The LUR models resulted in adjusted R2 of 0.737 and 0.704, respectively. The correlation of the predicted exposure values for cohort members was 0.96. The association of each 10 μg/m3 increase in NO2 with mortality was 6 % for 1995/96 and 4 % for 2007 LUR models. The increased risk per an inter-quartile range change was identical (4 %, 95 % CI:3–6 %) for both estimates of NO2.ConclusionsMeasured and predicted NO2 values from LUR models, from samples collected 12 years apart, had good agreement, and the exposure estimates were similarly associated with mortality in a large cohort study.