Ambient air pollution and term birth weight in Texas from 1998 to 2004

Previous studies have explored the association between air pollution levels and adverse birth outcomes such as lower birth weight. Existing literature suggests an association, although results across studies are not consistent. Additional research is needed to confirm the effect, investigate the exposure window of importance, and distinguish which pollutants cause harm.

We assessed the association between ambient pollutant concentrations and term birth weight for 1,548,904 births in TX from 1998 to 2004. Assignment of prenatal exposure to air pollutants was based on maternal county of residence at the time of delivery. Pollutants examined included particulate matter with aerodynamic diameter ≤10 and ≤2.5 µm (PM₁₀ and PM_2.5), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃). We applied a linear model with birth weight as a continuous variable. The model was adjusted for known risk factors and region. We assessed pollutant effects by trimester to identify biological exposure window of concern, and explored interaction due to race/ethnicity.

An interquartile increase in ambient pollutant concentrations of SO₂ and O₃ was associated with a 4.99-g (95% confidence interval [CI], 1.87–8.11) and 2.72-g (95% CI, 1.11–4.33) decrease in birth weight, respectively. Lower birth weight was associated with exposure to O₃ in the first and second trimester, whereas results were not significant for other pollutants by trimester. A positive association was exhibited for PM_2.5 in the first trimester. Effects estimates for PM₁₀ and PM_2.5 were inconsistent across race/ethnic groups.

Current ambient air pollution levels may be increasing the risk of lower birth weight for some pollutants. These risks may be increased for certain racial/ethnic groups. Additional research including consideration of improved methodology is needed to investigate these findings. Future studies should examine the influence of residual confounding.

Implications: This is one of the most comprehensive studies examining criteria air pollutants and lower birth weight in Texas. Our findings confirm results found previously for adverse effects of the air pollutant SO₂ on lower birth weight. Results from our study suggest that adverse pregnancy outcomes such as lower birth weight can occur even while maintaining air pollution levels below regulatory standards. Future studies should incorporate the assessment of differential pollutant exposure as well as effect estimates by race/ethnicity with individual and community-level social factors in order to enhance our understanding of how physical, social, and host factors influence birth outcomes.

Supplemental Materials: Supplementary information relating to characteristics of excluded births, distribution of air pollutant monitors by pollutant, and correlation coefficients of the air pollutants is available in the publisher's online edition of the Journal of the Air & Waste Management Association.     

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.     

Modeling the intra-urban variability of outdoor traffic pollution in Oslo,Norway—A GA2LEN project

Traffic is a major source of air pollutants in urban environments, and exposure to these pollutants may be associated with adverse health effects. However, inconsistencies in observational epidemiological studies may be caused by differential measurement errors in various approaches in assessing exposure.We aimed to evaluate a simple method for assessing outdoor air pollutant concentrations in Oslo, Norway, through a land-use regression method.Samples of nitrogen oxides (NO_x) were collected in two different weeks using Ogawa passive diffusion samplers simultaneously at 80 locations across Oslo. Independent variables used in subsequent regression models as predictors of the pollutants were derived using the Arc 9 geographic information system (GIS) software. Indicators of land use, traffic, population density, and physical geography were tested.The final regression model yielded an adjusted coefficient of determination (R²) of 0.77 for nitrogen dioxide (NO₂), 0.66 for nitric oxide (NO), and 0.73 for NO_x.The results suggest that a good predictive exposure model can be derived from this approach, which can be used to estimate long-term small-area variation in concentrations for individual exposure assessment in epidemiological studies in a highly cost-effective way. These small-area variations in traffic pollution are important since they may have associations with health effects.     

Daily Mortality in the Philadelphia Metropolitan Area and Size-Classified Particulate Matter

ABSTRACT

Time-series of daily mortality data from May 1992 to September 1995 for various portions of the seven-county Philadelphia, PA, metropolitan area were analyzed in relation to weather and a variety of ambient air quality parameters. The air quality data included measurements of size-classified PM, SO₄ ^2-, and H+ that had been collected by the Harvard School of Public Health, as well as routine air pollution monitoring data. Because the various pollutants of interest were measured at different locations within the metropolitan area, it was necessary to test for spatial sensitivity by comparing results for different combinations of locations. Estimates are presented for single pollutants and for multiple-pollutant models, including gaseous pollutants and mutually exclusive components of PM (PM_2.5 and coarse particles, SO₄ ^2- and non-SO₄ ^2- portions of total suspended particulate [TSP] and PM₁₀), measured on the day of death and the previous day.

We concluded that associations between air quality and mortality were not limited to data collected in the same part of the metropolitan area; that is, mortality for one part may be associated with air quality data from another, not necessarily neighboring, part. Significant associations were found for a wide variety of gaseous and particulate pollutants, especially for peak O₃. Using joint regressions on peak O₃ with various other pollutants, we found that the combined responses were insensitive to the specific other pollutant selected. We saw no systematic differences according to particle size or chemistry. In general, the associations between daily mortality and air pollution depended on the pollutant or the PM metric, the type of collection filter used, and the location of sampling. Although peak O₃ seemed to exhibit the most consistent mortality responses, this finding should be confirmed by analyzing separate seasons and other time periods.     

Spatial variation of airborne pollutant concentrations in Brisbane,Australia and its potential impact on population exposure assessment

Spread of air pollution sources and non-uniform mixing conditions in urban or regional air sheds often result in spatial variation of pollutant concentrations over different parts of the air sheds. A comprehensive understanding of this variation of concentrations is imperative for informed planning, monitoring and assessment in a range of critical areas including assessment of monitoring network efficiency or assessment of population exposure variation as a function of the location in the city. The aims of this work were to study the citywide variability of pollutants as measured by “urban background” type monitoring stations and to interpret the results in relation to the applicability of the data to population exposure assessments and the network efficiency. A comparison between ambient concentrations of NO_x, ozone and PM₁₀ was made for three stations in the Brisbane air shed network. The best correlated between the three stations were ozone concentrations followed by NO_x concentration, with the worst correlations observed for PM₁₀. With a few exceptions correlations of all pollutants between the stations were statistically significant. Marginally better were the correlations for the lower concentrations of pollutants that represent urban background, over the correlations for higher concentrations, representing peak values. Implications of these findings on application of the monitoring data to air-quality management, as well as the need for further investigations has been discussed.     

Controls on hourly variations in urban background air pollutant concentrations

Average 21st century concentrations of urban air pollutants linked to cardiorespiratory disease are not declining, and commonly exceed legal limits. Even below such limits, health effects are being observed and may be related to transient daytime peaks in pollutant concentrations. With this in mind, we analyse >52,000 hourly urban background readings of PM₁₀ and pollutant gases throughout 2007 at a European town with legal annual average concentrations of common pollutants, but with a documented air pollution-related cardiorespiratory health problem, and demonstrate the hourly variations in PM₁₀, SO₂, NO_x, CO and O₃. Back-trajectory analysis was applied to track the arrival of exotic PM₁₀ intrusions, the main controls on air pollutants were identified, and the typical hourly pattern on ambient concentrations during 2007 was profiled. Emphasis was placed on “worst case” data (>90th percentile), when health effects are likely to be greatest. The data show marked daytime variations in pollutants result from rush-hour traffic-related pollution spikes, midday industrial SO₂ maxima, and afternoon O₃ peaks. African dust intrusions enhance PM₁₀ levels at whatever hour, whereas European PM incursions produce pronounced evening peaks due to their transport direction (across an industrial traffic corridor). Transient peak profiling moves us closer to the reality of personal outdoor exposure to inhalable pollutants in a given urban area. We argue that such an approach to monitoring data potentially offers more to air pollution health effect studies than using only 24 h or annual averages.     

A general method for evaluating the effects of air pollutants on lung cancer prevalence

ABSTRACT

It is widely accepted that some air pollutants are related to lung cancer prevalence. An effective method is proposed to quantitatively evaluate the effects of air pollutants and the interactions between them. The method consisted of three parts: data decomposition, comparable data generation and relationship inference. Firstly, very limited monitoring data published by Geographic Information System were applied to calculate the inhalable air pollution of relatively massive patient samples. Then the investigated area was partitioned into a number of districts, and the comparable data containing air pollutant concentrations and lung cancer prevalence in all districts were generated. Finally, the relationships between pollutants and lung cancer prevalence were concluded by an information fusion tool: Choquet integral. As an example, the proposed method was applied in the investigation of air pollution in Tianjin, China. Overall, SO₂, O₃ and PM_2.5 were the top three factors for lung cancer. And there was obvious positive interaction between O₃ and PM_2.5 and negative interaction among SO₂, O₃ and PM₁₀. The effect of SO₂ on men was larger than on women. O₃ and SO₂ were the most important factors for the adenocarcinoma and squamous cell carcinoma, respectively. The effect of SO₂ or NO₂ on squamous cell carcinoma is obviously larger than that on adenocarcinoma, while the effect of O₃ or PM_2.5 on adenocarcinoma is obviously larger than that on squamous cell carcinoma. The results provide important suggestions for management of pollutants and improvement of environmental quality. The proposed method without any parameter is general and easily realized, and it sets the foundation for further researches in other cities/countries.

Implications: For total lung cancer prevalence, male and female lung cancer prevalence, and adenocarcinoma and squamous cell carcinoma prevalence, the proposed method not only quantify the effect of single pollutant (SO₂, NO₂, CO, O₃, PM_2.5, and PM₁₀) but also reveals the correlations between different pollutants such as positive interaction or negative interaction. The proposed method without any geographic predictor and parameter is much easier to realize, and it sets the foundation for further research in other cities/countries. The study results provide important suggestions for the targeted management of different pollutants and the improvement of human lung health.     

Multiple regression analysis in modeling of columnar ozone in Peninsular Malaysia

This study aimed to predict monthly columnar ozone (O₃) in Peninsular Malaysia by using data on the concentration of environmental pollutants. Data (2003–2008) on five atmospheric pollutant gases (CO₂, O₃, CH₄, NO₂, and H₂O vapor) retrieved from the satellite Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) were employed to develop a model that predicts columnar ozone through multiple linear regression. In the entire period, the pollutants were highly correlated (R?=?0.811 for the southwest monsoon, R?=?0.803 for the northeast monsoon) with predicted columnar ozone. The results of the validation of columnar ozone with column ozone from SCIAMACHY showed a high correlation coefficient (R?=?0.752–0.802), indicating the model’s accuracy and efficiency. Statistical analysis was utilized to determine the effects of each atmospheric pollutant on columnar ozone. A model that can retrieve columnar ozone in Peninsular Malaysia was developed to provide air quality information. These results are encouraging and accurate and can be used in early warning of the population to comply with air quality standards.     

Binational school-based monitoring of traffic-related air pollutants in El Paso, Texas (USA) and Ciudad Juárez, Chihuahua (México)

Paired indoor and outdoor concentrations of fine and coarse particulate matter (PM), PM2.5 reflectance [black carbon(BC)], and nitrogen dioxide (NO₂) were determined for sixteen weeks in 2008 at four elementary schools (two in high and two in low traffic density zones) in a U.S.-Mexico border community to aid a binational health effects study. Strong spatial heterogeneity was observed for all outdoor pollutant concentrations. Concentrations of all pollutants, except coarse PM, were higher in high traffic zones than in the respective low traffic zones. Black carbon and NO₂ appear to be better traffic indicators than fine PM. Indoor air pollution was found to be well associated with outdoor air pollution, although differences existed due to uncontrollable factors involving student activities and building/ventilation configurations. Results of this study indicate substantial spatial variability of pollutants in the region, suggesting that children’s exposures to these pollutants vary based on the location of their school.     

Ambient air quality levels in Tehran, Iran, from 1988 to 1993

The objectives of this study were to describe trends in ambient air quality in Tehran between 1988 and 1993, to determine if these levels exceeded the World Health Organization (WHO) guidelines, and to discuss possible health effects related to exposure for these particular pollutants. Data were acquired from Iran's Environmental Protection Agency (IEPA) and the Ministry of Health (MH). These agencies operate five automated ambient air monitoring stations located in areas with heavy traffic. Daily samples of SO₂, NO₂, CO, total suspended particulate matter (TSM), and hydrocarbons (HC) were collected to provide 24 hour averages for each pollutant. Every three months, mean concentrations were reported to IEPA. Composite samples from all five stations were stored in a databank operated by IEPA. The ambient air quality guidelines were obtained from WHO reports. Statistical analysis was carried out using a regression model, which was designed to fit the air pollution data and take into account missing data. The results showed that there was a statistically significant upward trend in air pollution levels for all of the measured pollutants, except NO2, during the years 1988 to 1993. WHO guidelines were routinely and substantially exceeded by all pollutants except TSM. These findings suggest that as the population continues to grow, and increasing numbers of motor vehicles are driven in Tehran, there is concern for the health effects that may result from exposure to these pollutants.     

Development of an individual exposure model for application to the Southern California children's health study

The Southern California Children's Health Study (CHS) investigated the relationship between air pollution and children's chronic respiratory health outcomes. Ambient air pollutant measurements from a single CHS monitoring station in each community were used as surrogates for personal exposures of all children in that community. To improve exposure estimates for the CHS children, we developed an Individual Exposure Model (IEM) to retrospectively estimate the long-term average exposure of the individual CHS children to CO, NO₂, PM₁₀, PM_2.5, and elemental carbon (EC) of ambient origin. In the IEM, pollutant concentrations due to both local mobile source emissions (LMSE) and meteorologically transported pollutants were taken into account by combining a line source model (CALINE4) with a regional air quality model (SMOG). To avoid double counting, local mobile sources were removed from SMOG and added back by CALINE4. Limited information from the CHS survey was used to group each child into a specific time-activity category, for which corresponding Consolidated Human Activity Database (CHAD) time-activity profiles were sampled. We found local traffic significantly increased within-community variability of exposure to vehicle-related pollutants. PM-associated exposures were influenced more by meteorologically transported pollutants and local non-mobile source emissions than by LMSE. The overall within-community variability of personal exposures was highest for NO₂ (±20–40%), followed by EC (±17–27%), PM₁₀ (±15–25%), PM_2.5 (±15–20%), and CO (±9–14%). Between-community exposure differences were affected by community location, traffic density, and locations of residences and schools in each community. Proper siting of air monitoring stations relative to emission sources is important to capture community mean exposures.     

The Precision Associated with the Sampling Frequency of Log-Normally Distributed Air Pollutant Measurements

The frequency of air monitoring necessary to characterize an air pollutant for a given time period and area is an important problem. This paper deals with the precision of measuring an air pollutant concentration. Past research has shown that the distribution of many air pollutants can be described as log-normal. Using this result equations have been developed that predict the precision of the sample mean of the air pollutant as a function of: the frequency of sampling, the standard deviation of the logarithms of the air pollution measurements, and the level of confidence. An illustration is given to demonstrate their use. The equations are used to compare sampling plans. Tables are presented showing the precision associated with five sampling plans, for three geometric standard deviations, for three levels of confidence, and five periods of time over which the sampling plan is employed.

In an Appendix a mathematical development is presented showing the theoretical derivation of the equations. With these equations the precision of a sampling plan can be determined for any level of confidence or period of time. All that is needed is an estimate of the geometric standard deviation for the air pollution measurements.

Finally, the theoretical model is applied to air monitoring data that were collected at Roselawn School in Cincinnati, Ohio, between January 3, 1968, and April 1, 1968. The 90-day period was divided into three 30-day periods. All possible samples of size three were taken from each of the 30-day periods and their means and confidence intervals were calculated. The number of times the confidence intervals contained the true means was determined. The actual number of samples accepted as having contained the true mean, for the 80, 90, 95, and 9 9% level of confidence compared favorably with the theoretical. It is concluded that the model adequately described the behavior of air pollutants.     

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

A Model for Uptake of Pollutants by Vegetation

A mass transfer approach is used in developing a practical mathematical model of gaseous pollutant uptake by leaves in which a series of resistances is summed across a concentration difference. The body of information presented in this paper is directed to plant pathologists or physiologists in the field of vegetal-pollutant effects and to people interested in the natural removal of air pollutants by vegetation. Correlations are given to calculate the aerodynamic and the stomatal resistances to uptake, while both a qualitative investigation and quantitative estimates are made of the mesophyllic resistance. The factors which control the aerodynamic resistance, r_a, are leaf size and wind speed, while the leaf physiology is the determinant of the stomatal resistance, r_s . It is noted that the chemical reaction rate and pollutant diffusivity in the mesophyll control the mesophyllic resistance, r_m, though the overall gas phase mesophyllic resistance, Hr_m, is strongly a function of pollutant solubility in water. Finally, the overall model is compared to earlier experimental work on vegetal uptake of SO₂.     

Effects of public health interventions on industrial emissions and ambient air in Cartagena, Spain

Background, aim, and scope

Ten years of public health interventions on industrial emissions to clean air were monitored for the Mediterranean city of Cartagena. During the 1960s, a number of large chemical and non-ferrous metallurgical factories were established that significantly deteriorated the city’s air quality. By the 1970s, the average annual air concentration of sulfur dioxide (SO₂) ranged from 200 to 300 µg/m³ (standard conditions units). In 1979, the Spanish government implemented an industrial intervention plan to improve the performance of factories and industrial air pollution surveillance. Unplanned urban development led to residential housing being located adjacent to three major factories. Factory A produced lead, factory B processed zinc from ore concentrates, and factory C produced sulfuric acid and phosphates. This, in combination with the particular abrupt topography and frequent atmospheric thermal inversions, resulted in the worsening of air quality and heightening concern for public health. In 1990, the City Council authorized the immediate intervention at these factories to reduce or shut down production if ambient levels of SO₂ or total suspended particles (TSP) exceeded a time-emission threshold in pre-established meteorological contexts. The aim of this research was to assess the appropriateness and effectiveness of the intervention plan implemented from 1992 to 2001 to abate industrial air pollution.

Materials and methods

The maximum daily 1-h ambient air level of SO₂, NO₂, and TSP pollutants was selected from one of the three urban automatic stations, designed to monitor ambient air quality around industrial emissions sources. The day on which an intervention took place to reduce and/or interrupt industrial production by factory and pollutant was defined as a control day, and the day after an intervention as a post-control day. To assess the short-term intervention effect on air quality, an ecological time series design was applied, using regression analysis in generalized additive models, focusing on day-to-day variations of ambient air pollutants levels. Two indicators were estimated: (a) appropriateness, the ratio between mean levels of the pollutant for control days versus the other days, and (b) effectiveness, the ratio between mean levels of the pollutant for post-control days versus the other days. Ratios in regression analyses were adjusted for trend, seasonality, temperature, humidity and atmospheric pressure, calendar day, and special events as well as the other pollutants.

Results

A total of 702 control days were made on the factories’ industrial production during the 10-year period. Fifteen reductions and five shutdown control days took place at factory A for ambient air SO₂. At factory B, more controls were carried out for the SO₂ pollutant in the years 1992–1993 and 1997. At factory C, the control days for SO₂ decreased from 59 reductions and 14 shutdowns to a minimum from 1995 onwards, whereas the controls on TSP were more frequent, reaching a maximum of 99 reductions and 47 shutdowns in the last year. SO₂ ambient air mean levels ranged from 456 to 699 µg/m³ among factories on reduction control days and between 624 and 1,010 µg/m³ on shutdown days. The TSP ambient air mean levels were 428 and 506 µg/m³ on reduction and shutdown days, respectively. For all types of control days and factories, a mean ratio of 104% (95% confidence interval [CI] 88 to 121) in SO₂ levels was obtained and a mean ratio of 67% (95% CI 59 to 75) in TSP levels. Post-control days at all factories showed a mean ratio of ?16% (95% CI ?7 to ?24) in SO₂ levels and a mean ratio of ?13% (95% CI ?7 to ?19) in TSP levels.

Discussion

Interventions on industrial production based on the urban SO₂ and TSP ambient air levels were justified by the high concentrations detected. The best assessment of the interventions’ effectiveness would have been to utilize the ambient air pollutant concentration readings from the entire time of the production shutdowns or reductions; however, the daily hourly maximum turned out to be a useful indicator because of meteorological factors influencing the diurnal concentration profile. A substantial number of interventions were carried out from 1 to 3 am, when vehicular traffic was minimum. On the other hand, atmospheric stability undergoes diurnal cycling in the autumn–winter period due to thermal inversion, which reaches maximum levels around daybreak. Therefore, this increases the ambient air levels and justified the interventions carried out at daybreak in spite of the traffic influence.

Conclusions

All the interventions for SO₂ and TSP were carried out when the measured ambient air levels of pollutants were exceeded, which shows the appropriateness of the intervention program. This excess was greater when intervening on SO₂ than on the TSP levels. For both ambient air levels of SO₂ and TSP, significant drops in air pollution were achieved from all three factories following activity reductions. The production shutdown controls were very effective, because they returned excess levels, higher than in the reduction controls, to everyday mean values.

Recommendations and perspectives

The Cartagena City observational system of intermittent control has proven to effectively reduce industrial emissions’ impact on ambient air quality. This experienced model approach could serve well in highly polluted industrial settings. From a public health perspective, studies are needed to assess that the industrial interventions to control air pollution were related to healthier human populations. Legislation was needed to allow the public administration to take direct actions upon the polluting industries.     

Effects of Instrument Precision and Spatial Variability on the Assessment of the Temporal Variation of Ambient Air Pollution in Atlanta,Georgia

Abstract

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO₂, CO, NO_x, and O₃) and fine particulate matter ([PM_2.5] PM_2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7–40% of the temporal variation in the daily pollutant measures and was largest for the PM_2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO₂, CO, NO_x, and EC). Population–weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O₃ to 70% of the temporal variation for SO₂ and EC. Wind rose plots, corrected for diurnal and seasonal pattern effects, are used to demonstrate the impacts of local sources on monitoring station data. The results presented are being used to quantify the impacts of instrument precision and spatial variability on the assessment of health effects of ambient air pollution in Atlanta and are relevant to the interpretation of results from time series health studies that use data from fixed monitors.     

Air pollution in cities

Air quality in cities is the result of a complex interaction between natural and anthropogenic environmental conditions. Air pollution in cities is a serious environmental problem – especially in the developing countries. The air pollution path of the urban atmosphere consists of emission and transmission of air pollutants resulting in the ambient air pollution. Each part of the path is influenced by different factors. Emissions from motor traffic are a very important source group throughout the world. During transmission, air pollutants are dispersed, diluted and subjected to photochemical reactions. Ambient air pollution shows temporal and spatial variability. As an example of the temporal variability of urban air pollutants caused by motor traffic, typical average annual, weekly and diurnal cycles of NO, NO₂, O₃ and O_x are presented for an official urban air-quality station in Stuttgart, southern Germany. They are supplemented by weekly and diurnal cycles of selected percentile values of NO, NO₂, and O₃. Time series of these air pollutants give information on their trends. Results are discussed with regard to air pollution conditions in other cities. Possibilities for the assessment of air pollution in cities are shown. In addition, a qualitative overview of the air quality of the world's megacities is given.     

Simulating Urban-Scale Air Pollutants and Their Predicting Capabilities over the Seoul Metropolitan Area

Abstract

Urban-scale air pollutants for sulfur dioxide, nitrogen dioxide, particulate matter with aerodynamic diameter >10 μm, and ozone (O₃) were simulated over the Seoul metropolitan area, Korea, during the period of July 2-11, 2002, and their predicting capabilities were discussed. The Air Pollution Model (TAPM) and the highly disaggregated anthropogenic and the biogenic gridded emissions (1 km × 1 km) recently prepared by the Korean Ministry of Environment were applied. Wind fields with observational nudging in the prognostic meteorological model TAPM are optionally adopted to comparatively examine the meteorological impact on the prediction capabilities of urban-scale air pollutants. The result shows that the simulated concentrations of secondary air pollutant largely agree with observed levels with an index of agreement (IOA) of >0.6, whereas IOAs of ～0.4 are found for most primary pollutants in the major cities, reflecting the quality of emission data in the urban area. The observationally nudged wind fields with higher IOAs have little effect on the prediction for both primary and secondary air pollutants, implying that the detailed wind field does not consistently improve the urban air pollution model performance if emissions are not well specified. However, the robust highest concentrations are better described toward observations by imposing observational nudging, suggesting the importance of wind fields for the predictions of extreme concentrations such as robust highest concentrations, maximum levels, and >90th percentiles of concentrations for both primary and secondary urban-scale air pollutants.