Influence of meteorological factors on air pollution concentration for a coastal region in India

A long-term study of measurement of concentration of NO_x, SO₂ and TSP pollutants have been done in a port and harbour region in India. Monthly measurements of gaseous and particulate pollutants were made at six monitoring stations from January 1997 to December 2000. Meteorological data was also simultaneously collected. In this study, the relationship between monitored ambient air quality data and meteorological factors, such as wind speed, temperature, is statistically analysed, using the SPSS package. The monthly mean concentrations of NO_x, SO₂ and TSP were in the range of 19.5–59.0 μg/m3, 8.6–51.3 μg/m3 and 88.2–199.3 μg/m3, respectively. The results show that TSP is strongly correlated with NO_x and SO₂ with a correlation coefficient of 0.83 and 0.82, respectively. The correlation coefficients for TSP, NO_x, and SO₂ with wind are –0.78, –0.78, and –0.88, respectively.     

Effect of air pollution on peri-urban agriculture: a case study

Peri-urban agriculture is vital for the urban populations of many developing countries. Increases in both industrialization and urbanization, and associated air pollution threaten urban food production and its quality. Six hour mean concentrations were monitored for SO(2), NO(2) and O(3) and plant responses were measured in terms of physiological characteristics, pigment, biomass and yield. Parameter reductions in mung bean (Vigna radiata), palak (Beta vulgaris), wheat (Triticum aestivum) and mustard (Brassica compestris) grown within the urban fringes of Varanasi, India correlated directly with the gaseous pollutants levels. The magnitude of response involved all three gaseous pollutants at peri-urban sites; O(3) had more influence at a rural site. The study concluded that air pollution in Varanasi could negatively influence crop yield.     

Involving the public in local air pollution assessment: a citizen participation case study

The central theme of this paper is that the assessment of environmental issues requires citizen participation for several reasons, including access to local knowledge, quality assurance and to reveal values and agendas. One methodology for involving the public is presented, based around the case of air pollution assessment in Sheffield. The study procedure of using citizen groups is described, and an analysis of the material from the groups is given. The final section argues that group discussions can be an appropriate methodology for involving the public in environmental assessment, and that the participants were able to comment on the validity and quality of such technical activities as continuous monitoring and computer modelling of air pollution in an urban environment.     

Analysis of the air pollution climate at a central urban background site

Measurements of air pollutants from a background site in central London are analysed. These comprise hourly data for CO, NO, NO₂, O₃, SO₂ and PM₁₀ from 1996 to 2008 and particle number count from 2001 to 2008. The data are analysed in terms of long-term trends, annual, weekly and diurnal cycles, and autocorrelation and cross-correlation functions. CO, NO and NO₂ show a typical traffic-associated pattern with two daily peaks and lesser concentrations at the weekend. Particle number count and PM₁₀ show a similar cycle, but with smaller amplitude. Ozone has an annual cycle with a maximum in May, influenced by the spring maximum in background ozone, but the diurnal and weekly cycles are dominated by losses through reaction with nitric oxide. Particle number count shows a minimum corresponding with maximum air temperatures in August, whereas the CO, NO NO₂ and SO₂ show a minimum in June/July. There is a lower particle count to NO_x ratio at the background site compared to a central London kerbside site (Marylebone Road) and a seasonal pattern in particle count to NO_x and PM₁₀ ratios consistent with loss of nanoparticles by evaporation during atmospheric transport. Sulphur dioxide peaks in the morning in summer, but at midday in winter consistent with emissions from elevated sources mixing down from aloft as the diurnal mixed layer deepens. Implications for epidemiological studies of air quality and health are discussed. Sulphur dioxide, carbon monoxide, nitric oxide and nitrogen dioxide show clear downward trends over the measurement period, PM₁₀ declines initially before levels stabilised, and ozone concentrations increased.     

Seasonal variation of air pollution index: Hong Kong case study

Air pollution is an important and popular topic in Hong Kong as concerns have been raised about the health impacts caused by vehicle exhausts in recent years. In Hong Kong, sulphur dioxide SO2, nitrogen dioxide (NO2), nitric oxide (NO), carbon monoxide (CO), and respirable suspended particulates (RSP) are major air pollutants caused by the dominant usage of diesel fuel by goods vehicles and buses. These major pollutants and the related secondary pollutant, e.g., ozone (O3), become and impose harmful impact on human health in Hong Kong area after the northern shifting of major industries to Mainland China. The air pollution index (API), a referential parameter describing air pollution levels, provides information to enhance the public awareness of air pollutions in time series since 1995. In this study, the varying trends of API and the levels of related air pollutants are analyzed based on the database monitored at a selected roadside air quality monitoring station, i.e., Causeway Bay, during 1999-2003. Firstly, the original measured pollutant data and the resultant APIs are analyzed statistically in different time series including daily, monthly, seasonal patterns. It is found that the daily mean APIs in seasonal period can be regarded as stationary time series. Secondly, the auto-regressive moving average (ARMA) method, implemented by Box-Jenkins model, is used to forecast the API time series in different seasonal specifications. The performance evaluations of the adopted models are also carried out and discussed according to Bayesian information criteria (BIC) and root mean square error (RMSE). The results indicate that the ARMA model can provide reliable, satisfactory predictions for the problem interested and is expecting to be an alternative tool for practical assessment and justification.     

Progress in developing an ANN model for air pollution index forecast

An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO₂ and NO_x, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM₁₀, SO₂, and NO₂. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.     

Analysis of air pollution data at a mixed source location using boosted regression trees

This paper explores the use of boosted regression trees to draw inferences concerning the source characteristics at a location of high source complexity. Models are developed for hourly concentrations of nitrogen oxides (NO_X) close to a large international airport. Model development is discussed and methods to quantify model uncertainties developed. It is shown that good explanatory models can be developed and further, allowing for interactions between model variables significantly improves the model fits compared with non-interacting models. Methods are used to determine which variables exert most influence over predicted concentrations and to explore the NO_X dependency for each. Model predictions are used to estimate aircraft take-off contributions to total concentrations of NO_X and determine how these predictions are affected by annual variations in meteorological conditions and runway use patterns. Furthermore, the results relating to the aircraft contributions to total NO_X concentration are compared with those from a more detailed independent field campaign. Finally, we find empirical evidence that plumes from larger aircraft disperse more rapidly from the point of release compared with smaller aircraft. The reasons for this behaviour and the implications are discussed.     

Analysis of atmospheric conditions during air pollution episodes in Ankara, Turkey

This paper presents a detailed analysis of the meteorological conditions that are associated with strong air pollution episodes in Ankara, Turkey. Based on climatological and air quality data [SO2 and TSP (total suspended particulates)] obtained for the winter months during 1989-1994, the analysis showed that the presence of weak atmospheric pressure gradients and warm air advection were the most important factors leading to high SO2 and TSP concentrations. In addition, the onset of the high air pollution episodes was generally associated with a trend toward negative vorticity at the 850-hPa level.     

Crop growth along a gradient of ambient air pollution

An experiment, designed to elucidate the relative importance of SO2, NO2, O3, and other environmental factors in influencing the performance of four cultivars of Trifolium pratense L. and Hordeum vulgare L., was performed by growing plants in situ along a transect from central London into the surrounding countryside. A multiple regression analysis provided evidence of significant effects of SO2, NO2, and, to a lesser extent, O3, on vegetative and reproductive growth parameters, although these differed according to pollutant, cultivar, species, and the parameter concerned. The significance of these findings for the impact of ambient air pollution on the growth of crops in the more polluted rural areas of western Europe is suggested by the fact that mean SO2, NO2, and O3 concentrations in the experimental area are less than 0.020 (39.2 microg/m3), 0.025 (47.75 microg/m3), and 0.030 ppm (58.8 microg/m3), respectively. The value of the technique is discussed with respect to other studies on the effects of low levels of air pollution on crops.     

Role of ambient air pollution in asthma spread among various population groups of Lahore City: a case study

Environmental Science and Pollution Research - Air pollution levels rise as a result of industrial and vehicular emissions, epidemiological issues such as asthma become more prevalent in Lahore,...     

Winter indoor air pollution in Alaska: identifying a myth

The benzene and toluene levels inside three homes with attached garages were measured for 12 consecutive weeks during the winter months in Fairbanks, Alaska (Latitude 64.5 degrees N). Results for air samples collected over 12 h for the homes showed indoor benzene mixing ratios ranging from 1.6 to 20.4 parts per billion of mixing ratio volume (ppbv), and toluene air mixing ratios ranging from 7.3 to 41.6 ppbv. A correlation between benzene and toluene levels in each home and similar regression lines suggested the same major emission source, car and small equipment gasoline, present in attached garages. In one home, there was a correlation between indoor benzene mixing ratios and the urinary biomarker, trans,trans-muconic acid. Inside, air mixing ratios of benzene and toluene decreased with decreasing outside temperature in all homes studied, even though homes were relatively tight to prevent heat loss during this period of low winter outdoor temperatures. It is suggested that buildup of these pollutants indoors is prevented by the influence of an increased indoor/outdoor temperature differential and an ensuing increase in home ventilation.     

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.     

Evaluation of air pollution trends in Istanbul

Previously, SO2 = SO₂ and PM₁₀ (particulate matter less than 10 Bm in size) concentration distributions have been investigated in order to assess air pollution in Istanbul during the winter season (November–March) in which the concentration of these pollutants had reached formidably high levels due to the consumption of low-quality fuels (mainly coal) for residential heating. In this study, the effect of the increased share of natural gas and high-quality coal consumption in residential areas on air pollution levels was investigated. Modelling employing the method of kriging by spherical interpolation was used to obtain the concentration distribution of these pollutants, and spatial distributions of concentrations were generated. The pollution map obtained by this method indicated that increased usage of natural gas and high-quality coal in residential areas significantly improved air quality.     

Analysis of coupled model uncertainties in source-to-dose modeling of human exposures to ambient air pollution: A PM2.5 case study

Quantitative assessment of human exposures and health effects due to air pollution involve detailed characterization of impacts of air quality on exposure and dose. A key challenge is to integrate these three components on a consistent spatial and temporal basis taking into account linkages and feedbacks. The current state-of-practice for such assessments is to exercise emission, meteorology, air quality, exposure, and dose models separately, and to link them together by using the output of one model as input to the subsequent downstream model. Quantification of variability and uncertainty has been an important topic in the exposure assessment community for a number of years. Variability refers to differences in the value of a quantity (e.g., exposure) over time, space, or among individuals. Uncertainty refers to lack of knowledge regarding the true value of a quantity. An emerging challenge is how to quantify variability and uncertainty in integrated assessments over the source-to-dose continuum by considering contributions from individual as well as linked components. For a case study of fine particulate matter (PM_2.5) in North Carolina during July 2002, we characterize variability and uncertainty associated with each of the individual concentration, exposure and dose models that are linked, and use a conceptual framework to quantify and evaluate the implications of coupled model uncertainties. We find that the resulting overall uncertainties due to combined effects of both variability and uncertainty are smaller (usually by a factor of 3–4) than the crudely multiplied model-specific overall uncertainty ratios. Future research will need to examine the impact of potential dependencies among the model components by conducting a truly coupled modeling analysis.     

Policies to cope with air pollution in Europe: theAuto-oil Programme in the case of Athens

Air pollution is one of the most crucial current environmental problems, especially in urban areas. To confront air pollution, the European Union has implemented several policies and programmes. Among these, the Auto-oil Programme is a cost-effective framework which involves all relevant actors (car-makers, oil industries, governments, local authorities, etc.) in concerted action. Athens faces a severe air pollution problem. In this respect, Auto-oil seems to offer a promising chance for Athens to improve its atmosphere. The present paper examines the effectiveness of the Auto-oil Programme to relieve air pollution in Athens. Several additional measures are proposed whenever it seems to be necessary.     

Responses of terrestrial arthropods to air pollution: a meta-analysis

Background, aim, and scope  

Arthropods, with over a million species described, are ubiquitous throughout different environments. Knowledge of their responses to human impact is crucial for understanding and predicting changes in ecosystem structure and functions. Our aim was to investigate the general patterns and to identify sources of variation in changes of the diversity, abundance and fitness of terrestrial arthropods (including Arachnida, Collembola and Insecta) in habitats affected by point polluters.     

Outdoor air pollution in close proximity to a continuous point source

Data are lacking on human exposure to air pollutants occurring in ground-level outdoor environments within a few meters of point sources. To better understand outdoor exposure to tobacco smoke from cigarettes or cigars, and exposure to other types of outdoor point sources, we performed more than 100 controlled outdoor monitoring experiments on a backyard residential patio in which we released pure carbon monoxide (CO) as a tracer gas for continuous time periods lasting 0.5–2 h. The CO was emitted from a single outlet at a fixed per-experiment rate of 120–400 cc min^?1 (～140–450 mg min^?1). We measured CO concentrations every 15 s at up to 36 points around the source along orthogonal axes. The CO sensors were positioned at standing or sitting breathing heights of 2–5 ft (up to 1.5 ft above and below the source) and at horizontal distances of 0.25–2 m. We simultaneously measured real-time air speed, wind direction, relative humidity, and temperature at single points on the patio. The ground-level air speeds on the patio were similar to those we measured during a survey of 26 outdoor patio locations in 5 nearby towns. The CO data exhibited a well-defined proximity effect similar to the indoor proximity effect reported in the literature. Average concentrations were approximately inversely proportional to distance. Average CO levels were approximately proportional to source strength, supporting generalization of our results to different source strengths. For example, we predict a cigarette smoker would cause average fine particle levels of approximately 70–110 μg m^?3 at horizontal distances of 0.25–0.5 m. We also found that average CO concentrations rose significantly as average air speed decreased. We fit a multiplicative regression model to the empirical data that predicts outdoor concentrations as a function of source emission rate, source–receptor distance, air speed and wind direction. The model described the data reasonably well, accounting for ～50% of the log-CO variability in 5-min CO concentrations.