Air Pollution Prediction Models Research Articles

Association of Short-term Exposure to Air Pollution With Mortality in Older Adults

The US Environmental Protection Agency is required to reexamine its National Ambient Air Quality Standards (NAAQS) every 5 years, but evidence of mortality risk is lacking at air pollution levels below the current daily NAAQS in unmonitored areas and for sensitive subgroups. To estimate the association between short-term exposures to ambient fine particulate matter (PM2.5) and ozone, and at levels below the current daily NAAQS, and mortality in the continental United States. Case-crossover design and conditional logistic regression to estimate the association between short-term exposures to PM2.5 and ozone (mean of daily exposure on the same day of death and 1 day prior) and mortality in 2-pollutant models. The study included the entire Medicare population from January 1, 2000, to December 31, 2012, residing in 39 182 zip codes. Daily PM2.5 and ozone levels in a 1-km × 1-km grid were estimated using published and validated air pollution prediction models based on land use, chemical transport modeling, and satellite remote sensing data. From these gridded exposures, daily exposures were calculated for every zip code in the United States. Warm-season ozone was defined as ozone levels for the months April to September of each year. All-cause mortality in the entire Medicare population from 2000 to 2012. During the study period, there were 22 433 862 million case days and 76 143 209 control days. Of all case and control days, 93.6% had PM2.5 levels below 25 μg/m3, during which 95.2% of deaths occurred (21 353 817 of 22 433 862), and 91.1% of days had ozone levels below 60 parts per billion, during which 93.4% of deaths occurred (20 955 387 of 22 433 862). The baseline daily mortality rates were 137.33 and 129.44 (per 1 million persons at risk per day) for the entire year and for the warm season, respectively. Each short-term increase of 10 μg/m3 in PM2.5 (adjusted by ozone) and 10 parts per billion (10-9) in warm-season ozone (adjusted by PM2.5) were statistically significantly associated with a relative increase of 1.05% (95% CI, 0.95%-1.15%) and 0.51% (95% CI, 0.41%-0.61%) in daily mortality rate, respectively. Absolute risk differences in daily mortality rate were 1.42 (95% CI, 1.29-1.56) and 0.66 (95% CI, 0.53-0.78) per 1 million persons at risk per day. There was no evidence of a threshold in the exposure-response relationship. In the US Medicare population from 2000 to 2012, short-term exposures to PM2.5 and warm-season ozone were significantly associated with increased risk of mortality. This risk occurred at levels below current national air quality standards, suggesting that these standards may need to be reevaluated.

Computation of geographic variables for air pollution prediction models in South Korea.

Recent cohort studies have relied on exposure prediction models to estimate individuallevel air pollution concentrations because individual air pollution measurements are not available for cohort locations. For such prediction models, geographic variables related to pollution sources are important inputs. We demonstrated the computation process of geographic variables mostly recorded in 2010 at regulatory air pollution monitoring sites in South Korea. On the basis of previous studies, we finalized a list of 313 geographic variables related to air pollution sources in eight categories including traffic, demographic characteristics, land use, transportation facilities, physical geography, emissions, vegetation, and altitude. We then obtained data from different sources such as the Statistics Geographic Information Service and Korean Transport Database. After integrating all available data to a single database by matching coordinate systems and converting non-spatial data to spatial data, we computed geographic variables at 294 regulatory monitoring sites in South Korea. The data integration and variable computation were performed by using ArcGIS version 10.2 (ESRI Inc., Redlands, CA, USA). For traffic, we computed the distances to the nearest roads and the sums of road lengths within different sizes of circular buffers. In addition, we calculated the numbers of residents, households, housing buildings, companies, and employees within the buffers. The percentages of areas for different types of land use compared to total areas were calculated within the buffers. For transportation facilities and physical geography, we computed the distances to the closest public transportation depots and the boundary lines. The vegetation index and altitude were estimated at a given location by using satellite data. The summary statistics of geographic variables in Seoul across monitoring sites showed different patterns between urban background and urban roadside sites. This study provided practical knowledge on the computation process of geographic variables in South Korea, which will improve air pollution prediction models and contribute to subsequent health analyses.

Systematic Approach for the Prediction of Ground-Level Air Pollution (around an Industrial Port) Using an Artificial Neural Network

The prediction of air pollution levels is critical to enable proper precautions to be taken before and during certain events. In this paper a rigorous method of preparing air quality data is proposed to achieve more accurate air pollution prediction models based on an artificial neural network (ANN). The models consider the prediction of daily concentrations of various ground-level air pollutants, namely CO, PM10, NO, NO2, NOx, SO2, H2S, and O3, which were measured by an ambient air quality monitoring station in Ghadafan village, located 700 m downwind of the emissions of Sohar Industrial Port on the Al-Batinah coast of Oman. The training of the models is based on the multi-layer perceptron (MLP) method with the Back-Propagation (BP) algorithm. The results show very good agreement between the actual and predicted concentrations, as the values of the coefficient of multiple determinations (R 2 ) for all ANN models exceeded 0.70. The results also show the importance of temperature in the daily variations of O3, SO2, and NOx, whilst the wind speed and wind direction play significant roles in the daily variations of NO, CO, NO2, and H2S. PM10 concentrations are influenced by almost all the measured meteorological parameters.

Air pollution prediction models of particles, As, Cd, Ni and Pb in a highly industrialized area in Castellón (NE, Spain)

The objective of this study was to elaborate a series of mathematical models with the aim of short-term prediction of TSP, PM10, As, Cd, Ni and Pb in ambient air. These pollutants depend on some known variables (meteorological variables). The reason for choosing this pollutant type is that particulate matter may present a much higher potential risk despite its low representativeness as compared with the gas pollutant group. A positive correlation between high particle concentrations and deterioration in public health has been shown in recent studies. The elements As, Cd, Ni and Pb in PM10 were also analyzed to determine the toxicity of these particles. The goal is to provide a useful instrument to alert the population facing possible episodes of high concentrations of atmospheric pollutants. The study was carried out in a highly industrialized area in the ceramic cluster of Castellon for 5 years (2001–2005). The origin of the contamination in this area is both natural and anthropogenic. The natural origin is due to the resuspension of mineral materials from the surrounding mountains and from the long-range transport of materials from North Africa. The anthropogenic contamination sources that stand out include the non-metallic mineral material industries (ceramic production), chemical industries (color, frit and enamel manufacturing), as well as vehicular traffic. Once the particle samples were collected in quartz fiber filters, the concentration levels of TSP and PM10 were determined gravimetrically. The chemical analysis of the filters was carried out by ICP-MS. Predictive models have been constructed by using multiple regression analysis together with time series models (ARIMA). The SPSS 14.0 statistical software has been employed to analyze the obtained experimental data.

A Synoptic Climatological Approach to Assess Climatic Impact on Air Quality in South-central Canada. Part I: Historical Analysis

Automated synoptic weather typing and robust orthogonal stepwise regression analysis (via principal components analysis) were applied together to develop within-weather-type air pollution prediction models for a variety of pollutants (specifically, carbon monoxide – CO, nitrogen dioxide – NO2, ozone – O3, sulphur dioxide – SO2, and suspended particles – SP) for the period 1974–2000 in south-central Canada. The SAS robust regression procedure was used to limit the influence of outliers on air pollution prediction algorithms. Six-hourly Environment Canada surface observed meteorological data and 6-hourly US National Centers for Environmental Prediction (NCEP) reanalysis data of various weather elements were used in the analysis. The models were developed using two-thirds of the total years for meteorological and air pollution data; the remaining one-third (randomly selected) was used for model validation. Robust stepwise regression analysis was performed to analytically determine the meteorological variables that might be used to predict air pollution concentrations. There was a significant correlation between observed daily mean air pollution concentrations and model predictions. About 20, 50, and 80% of the 80 prediction models across the study area possessed R 2 values ≥ 0.7, 0.6, and 0.5, respectively. The results of model validation were similar to those of model development, with slightly smaller model R 2 values.

A Synoptic Climatological Approach to Assess Climatic Impact on Air Quality in South-central Canada. Part II: Future Estimates

Using within-weather-group air pollution prediction models developed in Part I of this research, this study estimates future air pollution levels for a variety of pollutants (specifically, carbon monoxide – CO, nitrogen dioxide – NO2, ozone – O3, sulphur dioxide – SO2, and suspended particles – SP) under future climate scenarios for four cities in south-central Canada. A statistical downscaling method was used to downscale five general circulation model (GCM) scenarios to selected weather stations. Downscaled GCM scenarios were used to compare respective characteristics of the weather groups developed in Part I; discriminant function analysis was used to allocate future days from two windows of time (2040–2059 and 2070–2089) into one of four weather groups. In Part I, the four weather groups were characterised as hot, cold, air pollution-related, and other (defined as relatively good air quality and comfortable weather conditions). In estimating future daily air pollution concentrations, three future pollutant emission scenarios were considered: Scenario I – emissions decreasing 20% by 2050, Scenario II – future emissions remaining at the same level as at the end of the twentieth century, and Scenario III – emissions increasing 20% by 2050. The results showed that, due to increased temperatures, the average annual number of days with high O3 levels in the four selected cities could increase by more than 40–100% by the 2050s and 70–200% by the 2080s (from the current areal average of 8 days) under the three pollutant emission scenarios. The corresponding number of low O3 days could decrease by 4–10% and 5–15% (from the current areal average of 312 days). For the rest of the pollutants, future air pollution levels will depend on future pollutant emission levels. Under emission Scenarios II and III, the average annual number of high pollution days could increase 20–40% and 80–180%, respectively, by the middle and late part of this century. In contrast, under Scenario I, the average annual number of high pollution days could decrease by 10–65%.

Investigation of local meteorological events and their relationship with ozone and aerosols during an ESCOMPTE photochemical episode

Abstract. The international ESCOMPTE campaign, which took place in summer 2001 in the most highly polluted French region, was devoted to validate air pollution prediction models. Surface and remote sensing instruments (Lidar, Radar and Sodar) were deployed over the Marseille area, along the Mediterranean coast, in order to investigate the fine structure of the sea-breeze circulation and its relationship with the pollutant concentrations. The geographical situation of the Marseille region combines a complex coastline and relief which both lead to a peculiar behaviour of the sea-breeze circulation. Several local sea breezes, perpendicular to the nearest coastline, settled in during the morning. In the afternoons, when the thermal gradient between the continental and marine surface grows up, a southerly or a westerly sea breeze may dominate. Their respective importance is then a function of time, space and altitude. Furthermore, an oscillation of the westerly sea breeze with a period of about 3 h is also highlighted. We show that these dynamical characteristics have profound influences on the atmospheric boundary-layer (ABL) development and on pollutant concentrations. In fact, the direction and intensity of the sea-breeze determine the route and the transit time of the stable marine air flow over the continental surface. Thus, the ABL depth may exhibit several collapses correlated with the westerly sea-breeze pulsation. The ozone and aerosol concentrations are also related to the dynamical features. In the suburbs and parts of the city under pulsed sea breezes, a higher ABL depth and higher ozone concentrations are observed. In the city centre, this relationship between pulsed sea-breeze intensity and ozone concentration is different, emphasising the importance of the transit time and also the build-up of pollutants in the marine air mass along the route. Finally, the variations of aerosol concentration are also described according to the breeze direction.

Chronic Exposure to Particulate Matter and Cardiopulmonary Disease

TS3-06 Abstract: Although there is a substantial body of literature demonstrating the acute adverse health effects associated with air pollution, evidence regarding the effects of chronic exposure has been mainly limited to a few large cohort studies of mortality. The current study not only evaluates the relationship between particulate exposure and mortality, but also prospectively evaluates associations between chronic particulate exposures and cause-specific incident disease on a nationwide basis. These relationships are being examined using data from the Nurses’ Health Study, an ongoing prospective cohort study of 121,700 women residing throughout the United States. Incident cases of cardiovascular disease, respiratory disease, and cancers diagnosed during the study period are identified routinely through the biennial self-administered questionnaire. Cases are confirmed by supplemental questionnaire and review of medical records. Mortality is reported by next of kin and obtained by regular searches of the National Death Index. Information on potential confounders and effect modifiers is collected on the biennial questionnaire. Nurses’ residential addresses from 1988 through 2002 were geocoded and used as the basis for assigning exposure estimates. Exposure estimates were developed from predictive models of particulate matter less than 10 μm in diameter (PM10) and of PM2.5 for the years 1988 through 2002. Our air pollution prediction models for monthly average PM10 use data by monitoring site from U.S. EPA's Air Quality System (AQS) and various other sources, including the IMPROVE network and Harvard research studies. The modeling process also includes GIS-generated variables for block group, tract, and county-level population density; distance to nearest road, elevation, land use/cover, and meteorologic variables. All data were imported into generalized additive statistical models (GAMs) with smooth terms of space, time (separate surfaces for each month), and the GIS variables. The modeling approach combines a monthly pollution surface (the spatial component) and constant (time-invariant) effects of predictors derived from the GIS (the nonspatial component). As nationwide PM2.5 monitoring data are not available until 1999, our prediction models for PM2.5 use available PM2.5 monitoring data, corrected observations of horizontal visual range, and information from the PM10 predictive models to estimate monthly averages for 1988 through 2002. Currently, Cox proportional hazards models are being used to examine the associations of PM10 and PM2.5 with fatal and nonfatal myocardial infarction, strokes, and all-cause mortality. Potential confounders and effect modifiers such as smoking, physical activity, body mass index (BMI), physician-diagnosed hypertension and diabetes, menopausal status have been explored in the modeling process.

Prediction of air pollution concentration using an in situ real time mixing height model

Mixing height (MH), which represents the dispersion depth of the atmospheric boundary layer, is a crucial input parameter in air pollution models. However, there is enormous uncertainty in its estimation since it is not a directly measurable variable. Generally, MH is estimated from the twice daily radiosonde measurements from the nearest meteorological station, especially in developing countries. But, these extrapolated values cause severe errors in prediction since MH is site and time dependent. In this paper, a simple in situ mixing height growth (IMG) model, which can estimate onsite real time values of MH from readily available surface measurements of wind and temperature, is applied to some commonly used air pollution prediction models. Box models (BM) are often used for large-scale predictions, but assume a constant lid height, though their accuracy is highly dependant upon its variation. IMG was applied to a photochemical box model, since ozone formation is strongly dependent upon insolation and is controlled by real time values of MH. The ozone concentrations predicted by IMG–BM showed a 13% improvement as compared to those estimated from the usual extrapolated radiosonde values. Further, gaussian diffusion model (GDM) is recommended in India and many other countries for regulatory use. Application of IMG to GDM for industries showed that the IMG model considerably improves the prediction accuracy and can be used in a cost effective manner.

Numerical modeling of pollutant transport and chemistry during a high-ozone event in northern Taiwan

An air pollution prediction model system (APOPS) is developed and applied to northern Taiwanwith complex terrain and local thermal circulations. It consists of a nonhydrostatic mesoscalemeteoro-logical model system (MMPMS) and a gas/aerosol transport and air quality model(GATAM). The basic processes relevant to modeling the urban air pollution problems such asmeteorology, dispersion, chemistry and deposition are solved at the same time on practicallythe same grid. The APOPS was tested on a high-ozone event in northern Taiwan on 16 November 1998. Comparison with observed surface winds shows able to predict local flowpatterns such as sea/land breezes and mountain-valley wind in this high air pollution episode.The predicted surface concentrations of ozone and other pollutants are compared with measuredvalues, and a fairly good agreement with the mean normalized biases of -6%, -11%, for onedaysimulation and for daytime, respectively, is obtained for ozone. Thus, it is confirmed thatthe APOPS can be utilized to predict urban air quality in complex terrain area.

Prediction of pesticide concentrations in the atmosphere using an atmospheric diffusion model (linear source plume model)

The foundational model to predict concentration of pesticides in the atmosphere outside of the sprayed area was developed using the results of measured concentration in the atmosphere, in reference to the atmospheric diffusion model utilized for the air pollution prediction model. The atmospheric diffusion model assumes that the applied area was a topographically flat farmland, that wind direction and wind speeds were constant, and the pesticide was constantly discharged from the emission line sources. Therefore the linear source plume model (LSPLM) was developed. The concentration in the atmosphere was predicted by assigning the property of the pesticides and various conditions of measurements to the model, and compared with the measured them, then the adaptability of the model was examined. As a result, the correlation between the measured value and the predicted value in paddy and forested areas was significant ( P<0.01) although deviations in the order of tens were observed, the measured value and the predicted value were generally in agreement.

Transport and environment interactions: the italian framework

With road traffic in Europe forecast to increase, strategies are needed to keep transportation sector growth within the bounds imposed by a sustainable development. Research is contributing through a large number of projects dealing with transport–environment interactions. This paper reviews international research activities in this field, focusing on technological innovations, air and noise pollution prediction models, and existing tools for socioeconomic evaluation of traffic impacts on the environment. In particular, research projects of the Second Special Project on Transport (PFT2) of the Italian National Research Council (CNR) are outlined.

Advanced Research on Transport-Environment Interactions

Since road traffic in Europe is forecast to increase in the future, articulated strategies are needed in order to keep transportation sector growth within the bounds imposed by a sustainable development. Scientific research is contributing to pursue this aim through a large number of projects dealing with transportation-environment interactions. This paper reviews international research activities in this field, focusing particularly on technological innovations, air and noise pollution prediction models as well as on existing tools for socio-economic evaluation of traffic impacts on the environment. In particular, research projects of the Second Special Project on Transportation (PFT2) of the Italian National Research Council (CNR) are outlined. The aim of this paper is to underline the most relevant research topics on which it is necessary to concentrate the efforts if it is expected the community welfare to be achieved.