An Index and Model of Human Response to Air Quality

This study undertook an empirical Investigation of human response to air quality. Home interviews of 475 respondents living in 22 neighborhoods of Los Angeles County had elicited information on respondent socioeconomic characteristics, behavioral patterns, and measures of human response to air quality. This data base was then augmented with nine measures of actual air quality for six time frames for each neighborhood.

An observer-based air quality index (OBAQI) was constructed based upon which combination of air quality variables correlated best with the percentage of neighborhood respondents who perceived “smoggy air.” The best combination (OBAQI 3) consisted of prevailing visibility, O₃, and SO₂, each measured as the annual number of days that a selected standard had been equalled or exceeded. Subsequently, multiple regression models were constructed using this index as a predictor of aggregated perception of air quality. In addition to a general model for all neighborhoods, separate models were constructed for clusters of neighborhoods of similar micrometeorology as approximated by uniform elevations and/or coastal distances. Zones of homogeneous air quality and micrometeorology were then defined. Within these zones variation in four measures of human response to air quality was associated with respondent socioeconomic characteristics and behavioral patterns. Both the index and model could prove useful in gauging public response to proposed actions of air quality management districts.     

Spatiotemporal analysis of COVID-19, air pollution,climate, and meteorological conditions in a metropolitan region of Iran

The COVID-19 pandemic has a close relationship with local environmental conditions. This study explores the effects of climate characteristics and air pollution on COVID-19 in Isfahan province, Iran. A number of COVID-19 positive cases, main air pollutants, air quality index (AQI), and climatic variables were received from March 1, 2020, to January 19, 2021. Moreover, CO, NO₂, and O₃ tropospheric levels were collected using Sentinel-5P satellite data. The spatial distribution of variables was estimated by the ordinary Kriging and inverse weighted distance (IDW) models. A generalized linear model (GLM) was used to analyze the relationship between environmental variables and COVID-19. The seasonal trend of nitrogen dioxide (NO₂), wind speed, solar energy, and rainfall like COVID-19 was upward in spring and summer. The high and low temperatures increased from April to August. All variables had a spatial autocorrelation and clustered pattern except AQI. Furthermore, COVID-19 showed a significant association with month, climate, solar energy, and NO₂. Suitable policy implications are recommended to be performed for improving people’s healthcare and control of the COVID-19 pandemic. This study could survey the local spread of COVID-19, with consideration of the effect of environmental variables, and provides helpful information to health ministry decisions for mitigating harmful effects of environmental change. By means of the proposed approach, probably the COVID-19 spread can be recognized by knowing the regional climate in major cities. The present study also finds that COVID-19 may have an effect on climatic condition and air pollutants.

     

Temporal and spatial statistical analysis of ambient air quality of Assam (India)

ABSTRACT

Present paper represents the spatio-temporal variation of air quality and performances of geostatistical tools for the identification of pollutants zone in various districts of Assam (India). Geographic Information System (GIS) and geostatistical analysis were utilized to estimate the spatio-temporal variations (2015–2017) of gaseous and particulate air pollutants. Data of 23 fixed monitoring stations were collected from the Central Pollution Control Board (CPCB). It was observed that SO₂ and NO_x concentrations are the major pollutants to the deterioration of air quality in Assam State. Exploratory data analysis was considered for the determination of spatial and temporal patterns of air pollutants. Air Quality index (AQI) was calculated based on the air pollutants and particulate matter. Radial Basis Function (RBF) interpolation techniques were used to analyze the spatial and temporal variation of air quality in Assam. Cross-validation is applied to evaluate the accuracy of interpolation methods in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash–Sutcliffe Equation (NSE) and Accuracy Factor (ACFT). In 2015, the high value of AQI portrayed in the central and northeast of the state. In 2016, the central and entire east of the study area was recorded the highest value of AQI. In 2017, it was observed that mostly the central part of the state recorded the high value of AQI. The spatio-temporal variation trend of air pollutants provides sound scientific basis for its management and control. This information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes.     

Spatio-temporal trends and influencing factors of PM2.5 concentrations in urban agglomerations in China between 2000 and 2016

An urban agglomeration (UA), similar to a megalopolis or a metropolitan area, is a region where cities and people are concentrated, and where air pollution has adversely impacted on sustainable and high quality development. Studies on the spatio-temporal trends and the factors which influence PM_2.5 concentrations may be used as a reference to support air pollution control policy for major UAs throughout the world. Nineteen UAs in China covering the years 2000–2016 were chosen as the research object, the PM_2.5 concentrations being used to reflect air pollution and being estimated from analysis of remote sensing images. The Exploratory Spatial Data Analysis method was used to study the spatio-temporal trends for PM_2.5 concentrations, and the Geodetector method was used to examine the factors influencing the PM_2.5 concentrations. The results revealed that (i) the temporal trend for the average values of the PM_2.5 concentrations in the UAs followed an inverted U-shaped curve and the inflection points of the curve occurred in 2007. (ii) The PM_2.5 concentrations in the UAs exhibited significant global spatial autocorrelation with the high–high type and the low–low type being the main categories. (iii) The rate of land urbanization and the structure of energy consumption were the main factors which influenced the PM_2.5 concentrations in the UAs.

     

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

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

Requirements for the completeness of ambient air quality data sets with respect to derived parameters

Monitoring and sampling of air quality data is costly and labor intensive. The necessary efforts increase progressively with increasing accuracy requirements. Also loss of data because of instrument break down, data transmission failure, or service and calibrating procedures is more or less unavoidable. Calculation of characteristic parameters like means or percentiles as necessary for information compression and also for comparison with air quality standards do not require complete data sets, since successive primary data like half-hour means are not independent from each other. Emission patterns and periodically reappearing or comparably slowly changing transmission conditions are responsible for autocorrelation of these data. Using air quality data from the Austrian public monitoring networks for various air pollutants (NO₂, SO₂, CO, O₃) over the last decade various patterns of data loss are simulated and used to compute air quality parameters (fractiles, semi-annual means, daily means). The variation interval of these parameters is compared to equivalent parameters resulting from the complete data sets. Furthermore, autocorrelation functions of these data are calculated and discussed briefly. Finally, the applicability of the parameters obtained from truncated data sets for air quality management decisions is discussed and compared to the Austrian standard. The results indicate an error of a few percent — depending on the type of data loss — if these parameters are computed from incomplete data sets up to 50% data loss. Thus reduction of monitoring efforts without substantial loss of information is possible.     

Quantification and Analysis of Airborne Bacterial Characteristics in a Nursing Care Institution

ABSTRACT

Indoor air quality has become a critical issue because people spend most of their time in the indoor environment. The factors that influence indoor air quality are very important to environmental sanitation and air quality improvement. This study focuses on monitoring air quality, colony counts, and bacteria species of the indoor air of a nursing care institution. The regular colony counts in two different wards range from 55 to 600 cfu m^?3. Regression analysis results indicate that the bacterial colony counts have close correlation with relative humidity or carbon dioxide (CO₂) but not with carbon monoxide (CO) or ozone (O₃). Real-time PCR was used to quantify the bacterial pathogens of nosocomial infection, including Acinetobacter baumannii, Citrobacter freundii, Escherichia coli, Klebsiella pneumoniae, and methicillin-sensitive Staphylococcus aureus. The most abundant bacteria species in the air of the nursing care institution is E. coli.

IMPLICATIONS Indoor temperature, humidity, ventilation, accumulation of biological pollutants, and potential infection problems will seriously affect the indoor environments. Studying these factors is important to indoor environmental sanitation and air quality improvements. Results of using real-time PCR to evaluate the bacterial pathogens of nosocomial infection for a nursing care institution in Taiwan reveal that the main bacteria species existing in the indoor air is E. coli.     

Air quality mapping using GIS and economic evaluation of health impact for Mumbai City,India

Mumbai, a highly populated city in India, has been selected for air quality mapping and assessment of health impact using monitored air quality data. Air quality monitoring networks in Mumbai are operated by National Environment Engineering Research Institute (NEERI), Maharashtra Pollution Control Board (MPCB), and Brihanmumbai Municipal Corporation (BMC). A monitoring station represents air quality at a particular location, while we need spatial variation for air quality management. Here, air quality monitored data of NEERI and BMC were spatially interpolated using various inbuilt interpolation techniques of ArcGIS. Inverse distance weighting (IDW), Kriging (spherical and Gaussian), and spline techniques have been applied for spatial interpolation for this study. The interpolated results of air pollutants sulfur dioxide (SO₂), nitrogen dioxide (NO₂) and suspended particulate matter (SPM) were compared with air quality data of MPCB in the same region. Comparison of results showed good agreement for predicted values using IDW and Kriging with observed data. Subsequently, health impact assessment of a ward was carried out based on total population of the ward and air quality monitored data within the ward. Finally, health cost within a ward was estimated on the basis of exposed population. This study helps to estimate the valuation of health damage due to air pollution.

Implications: Operating more air quality monitoring stations for measurement of air quality is highly resource intensive in terms of time and cost. The appropriate spatial interpolation techniques can be used to estimate concentration where air quality monitoring stations are not available. Further, health impact assessment for the population of the city and estimation of economic cost of health damage due to ambient air quality can help to make rational control strategies for environmental management. The total health cost for Mumbai city for the year 2012, with a population of 12.4 million, was estimated as USD8000 million.     

Analysis of air quality in Dire Dawa,Ethiopia

Ambient air quality was monitored and analyzed to develop air quality index and its implications for livability and climate change in Dire Dawa, Ethiopia. Using survey research design, 16 georeferenced locations, representing different land uses, were randomly selected and assessed for sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon dioxide (CO₂), carbon monoxide (CO),volatile organic compounds (VOCs), and meteorological parameters (temperature and relative humidity). The study found mean concentrations across all land uses for SO₂ of 0.37 ± 0.08 ppm, NO₂ of 0.13 ± 0.17 ppm, CO₂ of 465.65 ± 28.63 ppm, CO of 3.35 ± 2.04 ppm, and VOCs of 1850.67 ± 402 ppm. An air quality index indicated that ambient air quality for SO₂ was very poor, NO₂ ranged from moderate to very poor, whereas CO rating was moderate. Significant positive correlations existed between temperature and NO₂, CO₂, and CO and between humidity and VOCs. Significant relationships were also recorded between CO₂ and NO₂ and between CO and CO₂. Poor urban planning, inadequate pollution control measure, and weak capacity to monitor air quality have implications for energy usage, air quality, and local meteorological parameters, with subsequent feedback into global climate change. Implementation of programs to monitor and control emissions in order to reduce air pollution will provide health, economic, and environmental benefits to the city.

Implications: The need to develop and implement emission control programs to reduce air pollution in Dire Dawa City is urgent. This will provide enormous economic, health, and environmental benefits. It is expected that economic effects of air quality improvement will offset the expenditures for pollution control. Also, strategies that focus on air quality and climate change present a unique opportunity to engage different stakeholders in providing inclusive and sustainable development agenda for Dire Dawa.     

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.     

An Evaluation of CO2, Measurements as an Indicator of Air Pollution

Measurements of ambient carbon dioxide (CO₂), made at the Continuous Air Monitoring Program station in downtown Cincinnati, Ohio, and at a rural location near Cincinnati are presented and evaluated to determine the significance of CO₂ data in urban air quality monitoring programs. Through analysis of rural CO₂ data and evaluation of combustion-sources by means of a diffusion model, it is demonstrated that the variation of urban CO₂ concentrations around the prevailing atmosphere background level results from combustion-and noncombustion {natural) sources. The concentration from natural sources can be substantial and in fact override the combustion sources. Because it is not yet practical to predict the contribution of natural sources to urban CO₂ concentrations, data obtained for-this gas have only limited utility as an index of air quality. Significant statistical relationships between CO₂ data and air quality measurements for summer months are shown to result from similar meteorological effects rather than similar sources. A seasonal and spatial variation of ih-ese relationships is postulated and subsequently demonstrated by analysis of CO₂ and air quality measurements from New Orleans, Louisiana, and SU Louis, Missouri.     

Concentrations of particulate matter,carbon dioxide,VOCs and risk assessment inside Korean taxis and ships

The objective of this study was to investigate the concentration distribution of indoor air pollutants in taxis and ships (passengers) which are frequently used for public transportation and recreational activities in South Korea. In addition, it aimed to assess air quality factors to establish and evaluate the health risks of exposure to polluted indoor air. Particulate matter (PM₁₀) concentrations were not affected by the number of passengers, time of day, and driving characteristics because there were only a few passengers (2 to 4 people) and the space was confined. In the ships, indoor air pollutants responded more sensitively to the operation characteristics depending on the time of sailing (i.e., anchoring and departure, movement of vehicles on the ship, movement of passengers, combustion in the shop, and ventilation) than to the number of people boarding and alighting. The carbon dioxide concentrations in different ship rooms did not vary according to season and degree of congestion; however, there were differences between different ships. These differences may result from the size, type, and operating characteristics of the ships. Volatile organic compounds (VOCs) and aldehydes in new taxis exceeded the standard levels during summer. VOC concentrations in ships were particularly high during summer when the outdoor temperature was high. Similar observations were made for other means of transportation. The risk assessment depended on the means of transportation and demonstrated that mortality risks due to PM₁₀ and excess carcinogenic and non-carcinogenic risks from VOCs and aldehydes were within safety levels.

     

The Steubenville Comprehensive Air Monitoring Program (SCAMP): Overview and Statistical Considerations

Abstract

Average concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM_2.5) in Steubenville, OH, have decreased by more than 10 μg/m³ since the landmark Harvard Six Cities Study¹ associated the city’s elevated PM_2.5 concentrations with adverse health effects in the 1980s. Given the promulgation of a new National Ambient Air Quality Standard (NAAQS) for PM_2.5 in 1997, a current assessment of PM_2.5 in the Steubenville region is warranted. The Steubenville Comprehensive Air Monitoring Program (SCAMP) was conducted from 2000 through 2002 to provide such an assessment. The program included both an outdoor ambient air monitoring component and an indoor and personal air sampling component. This paper, which is the first in a series of four that will present results from the outdoor portion of SCAMP, provides an overview of the outdoor ambient air monitoring program and addresses statistical issues, most notably autocorrelation, that have been overlooked by many PM_2.5 data analyses. The average PM_2.5 concentration measured in Steubenville during SCAMP (18.4 μg/m³) was 3.4g/m³ above the annual PM_2.5 NAAQS. On average, sulfate and organic material accounted for ～31% and 25%, respectively, of the total PM_2.5 mass. Local sources contributed an estimated 4.6 μg/m³ to Steubenville’s mean PM_2.5 concentration. PM_2.5 and each of its major ionic components were significantly correlated in space across all pairs of monitoring sites in the region, suggesting the influence of meteorology and long-range transport on regional PM_2.5 concentrations. Statistically significant autocorrelation was observed among time series of PM_2.5 and component data collected at daily and 1-in-4-day frequencies during SCAMP. Results of spatial analyses that accounted for autocorrelation were generally consistent with findings from previous studies that did not consider autocorrelation; however, these analyses also indicated that failure to account for autocorrelation can lead to incorrect conclusions about statistical significance.     

Infant Mortality and Air Pollution: A Comprehensive Analysis of U.S. Data for 1990

ABSTRACT

This paper uses U.S. linked birth and death records to explore associations between infant mortality and environmental factors, based on spatial relationships. The analysis considers a range of infant mortality end points, regression models, and environmental and socioeconomic variables. The basic analysis involves logistic regression modeling of individuals; the cohort comprises all infants born in the United States in 1990 for whom the required data are available from the matched birth and death records. These individual data include sex, race, month of birth, and birth weight of the infant, and personal data on the mother, including age, adequacy of prenatal care, and smoking and education in most instances. Ecological variables from Census and other sources are matched on the county of usual residence and include ambient air quality, elevation above sea level, climate, number of physicians per capita, median income, racial and ethnic distribution, unemployment, and population density. The air quality variables considered were 1990 annual averages of PM₁₀, CO, SO₂, SO₄ ^2-, and “non-sulfate PM₁₀” (NSPM₁₀—obtained by subtracting the estimated SO₄ ^2-mass from PM₁₀). Because all variables were not available for all counties (especially maternal smoking), it was necessary to consider various subsets of the total cohort.

We examined all infant deaths and deaths by age (neonatal and postneonatal), by birth weight (normal and low [<2500 g]), and by specific causes within these categories. Special attention was given to sudden infant death syndrome (SIDS). For comparable modeling assumptions, the results for PM₁₀ agreed with previously published estimates; however, the associations with PM₁₀ were not specific to probable exposures or causes of death and were not robust to changes in the model and/or the locations considered. Significant negative mortality associations were found for SO₄ ^2-. There was no indication of a role for outdoor PM_2.5, but possible contributions from indoor air pollution sources cannot be ruled out, given higher SIDS rates in winter, in the north and west, and outside of large cities.     

A Method for Using Air Pollution Measurements and Public Opinion to Establish Ambient Air Quality Standards

Air quality was comprehensively evaluated by means of sampling at 21 locations over metropolitan Birmingham during a period of one year. Thousands of integrated samples of three common atmospheric gaseous pollutants and two common particulate pollutants were collected and analyzed. Following the year of sampling in 1964, a random household survey was completed by conducting personal interviews at more than 7200 households over metropolitan Birmingham. Statistical reduction of household survey results by census tract and by neighborhood area provided domestic fuel and waste burning emission data as well as public (resident) opinion on specific air pollution effects. The relationship between ambient air quality and neighborhood opinion of air pollution effects on health and property are evaluated statistically. Ambient standards are suggested which are based upon those air pollution levels shown to have adverse effects on approximately one-third of the people.     

Spatial heterogeneity of PM10 and O3 in São Paulo, Brazil, and implications for human health studies

Developing exposure estimates is a challenging aspect of investigating the health effects of air pollution. Pollutant levels recorded at centrally located ambient air quality monitors in a community are commonly used as proxies for population exposures. However, if ample intraurban spatial variation in pollutants exists, city-wide averages of concentrations may introduce exposure misclassification. We assessed spatial heterogeneity of particulate matter with an aerodynamic diameter < or = 10 microm (PM10) and ozone (O3) and evaluated implications for epidemiological studies in S?o Paulo, Brazil, using daily (24-hr) and daytime (12-hr) averages and 1-hr daily maximums of pollutant levels recorded at the regulatory monitoring network. Monitor locations were also analyzed with respect to a socioeconomic status index developed by the municipal government. Hourly PM10 and O3 data for the Sāo Paulo Municipality and Metropolitan Region (1999-2006) were used to evaluate heterogeneity by comparing distance between monitors with pollutants' correlations and coefficients of divergence (CODs). Both pollutants showed high correlations across monitoring sites (median = 0.8 for daily averages). CODs across sites averaged 0.20. Distance was a good predictor of CODs for PM10 (p < 0.01) but not O3, whereas distance was a good predictor of correlations for O3 (p < 0.01) but not PM10. High COD values and low temporal correlation indicate a spatially heterogeneous distribution of PM10. Ozone levels were highly correlated (r > or = 0.75), but high CODs suggest that averaging over O3 levels may obscure important spatial variations. Of municipal districts in the highest of five socioeconomic groups, 40% have > or = 1 monitor, whereas districts in the lowest two groups, representing half the population, have no monitors. Results suggest that there is a potential for exposure misclassification based on the available monitoring network and that spatial heterogeneity depends on pollutant metric (e.g., daily average vs. daily 1-hr maximum). A denser monitoring network or alternative exposure methods may be needed for epidemiological research. Findings demonstrate the importance of considering spatial heterogeneity and differential exposure misclassification by subpopulation.     

A real-time monitoring and assessment method for calculation of total amounts of indoor air pollutants emitted in subway stations

Subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, in this study, a new cumulative calculation method for the estimation of total amounts of indoor air pollutants emitted inside the subway station is proposed by taking cumulative amounts of indoor air pollutants based on integration concept. Minimum concentration of individual air pollutants which naturally exist in indoor space is referred as base concentration of air pollutants and can be found from the data collected. After subtracting the value of base concentration from data point of each data set of indoor air pollutant, the primary quantity of emitted air pollutant is calculated. After integration is carried out with these values, adding the base concentration to the integration quantity gives the total amount of indoor air pollutant emitted. Moreover, the values of new index for cumulative indoor air quality obtained for 1 day are calculated using the values of cumulative air quality index (CAI). Cumulative comprehensive indoor air quality index (CCIAI) is also proposed to compare the values of cumulative concentrations of indoor air pollutants. From the results, it is clear that the cumulative assessment approach of indoor air quality (IAQ) is useful for monitoring the values of total amounts of indoor air pollutants emitted, in case of exposure to indoor air pollutants for a long time. Also, the values of CCIAI are influenced more by the values of concentration of NO₂, which is released due to the use of air conditioners and combustion of the fuel. The results obtained in this study confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well.

Implications: Nowadays, subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in the indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, this paper presents a new methodology for monitoring and assessing total amounts of indoor air pollutants emitted inside underground spaces and subway stations. A new methodology for the calculation of cumulative amounts of indoor air pollutants based on integration concept is proposed. The results suggest that the cumulative assessment approach of IAQ is useful for monitoring the values of total amounts of indoor air pollutants, if indoor air pollutants accumulated for a long time, especially NO₂ pollutants. The results obtained here confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well.     

Air pollution characteristics associated with mesoscale atmospheric patterns in northwest continental Europe

An impact related daily air quality index (DAQx), calculated for 15 air quality monitoring stations (traffic, background, and industry) in Belgium, France, Germany and Luxembourg, was compared to mesoscale atmospheric patterns between 2001 and 2007. Meteorological conditions were described by the Hess and Brezowsky synoptic weather classification system and gridded data of the EU FP6 ENSEMBLES project of total precipitation and mean surface temperature. DAQx values indicate sufficient to poor air quality in the urban area of Brussels and at urban traffic stations, as well as satisfactory air quality at the background stations. The air quality index refers to more than 90% to the presence of high PM₁₀, O₃ and NO₂ concentrations. SO₂ and CO play only a minor role. The investigation of weather regimes indicates that zonal and mixed cyclonic circulation regimes are associated with better air quality than meridional and anticyclonic weather regimes. In general, weather regimes with high daily precipitation lead to better air quality than dryer air masses because of lower contribution of PM₁₀ to the air quality index. A trend analysis of weather regimes from 1978 to 2007 shows significant (α = 0.05) positive trends for weather classes associated with lower PM₁₀ concentrations. The results of a case study at a German station examining the relationship between PM₁₀ concentrations and local meteorological quantities (wind speed and precipitation) confirm the results of the regional analysis.     

Evaluation of environmental impact produced by different economic activities with the global pollution index

Introduction

The paper analyses the environment pollution state in different case studies of economic activities (i.e. co-generation electric and thermal power production, iron profile manufacturing, cement processing, waste landfilling, and wood furniture manufacturing), evaluating mainly the environmental cumulative impacts (e.g. cumulative impact against the health of the environment and different life forms).

Materials and methods

The status of the environment (air, water resources, soil, and noise) is analysed with respect to discharges such as gaseous discharges in the air, final effluents discharged in natural receiving basins or sewerage system, and discharges onto the soil together with the principal pollutants expressed by different environmental indicators corresponding to each specific productive activity. The alternative methodology of global pollution index (I _GP ^* ) for quantification of environmental impacts is applied.

Results and discussion

Environmental data analysis permits the identification of potential impact, prediction of significant impact, and evaluation of cumulative impact on a commensurate scale by evaluation scores (ES_i) for discharge quality, and global effect to the environment pollution state by calculation of the global pollution index (I _GP ^* ).

Conclusions

The I _GP ^* values for each productive unit (i.e. 1.664?C2.414) correspond to an ??environment modified by industrial/economic activity within admissible limits, having potential of generating discomfort effects??. The evaluation results are significant in view of future development of each productive unit and sustain the economic production in terms of environment protection with respect to a preventive environment protection scheme and continuous measures of pollution control.     

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial,California, USA

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM_2.5 and PM₁₀ (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R² for converted hourly averaged Dylos mass measurements versus a PM_2.5 BAM was 0.79 and that versus a PM₁₀ BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM_2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R² = 0.35–0.81).

Implications: The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.