Ground-level ozone mapping in large urban areas using multivariate statistical analysis: application to the São Paulo Metropolitan Area

A statistical study on the behavior of ground-level O3 concentration in different regions of a large urban area was carried out, with emphasis on pollutant gas concentrations and meteorological variables. The study was based on data generated by a network of measuring stations distributed throughout the S?o Paulo Metropolitan Area, in regions with different characteristics of traffic and economic activities. The combined application of principal component analysis and clustering techniques to data collected from 1997 until 2000 has led to the identification of implicit relationships between variables that have been associated with dominant processes related to O3 formation in different locations. Similarities between different regions of the city have also been detected and associated with local characteristics. The results indicate that the application of such statistical techniques to data collected in large urban areas enables the grouping of different regions according to their behavior in terms of O3 levels, as well as the identification of dominant processes in each group. These techniques are thus important in the planning of air pollution policies, especially in the case of O3, a pollutant that is not directly related to pollution levels alone.     

Ozone Air Quality over North America: Part II—An Analysis of Trend Detection and Attribution Techniques

ABSTRACT

Assessment of regulatory programs aimed at improving ambient O₃ air quality is of considerable interest to the scientific community and to policymakers. Trend detection, the identification of statistically significant long-term changes, and attribution, linking change to specific clima-tological and anthropogenic forcings, are instrumental to this assessment. Detection and attribution are difficult because changes in pollutant concentrations of interest to policymakers may be much smaller than natural variations due to weather and climate. In addition, there are considerable differences in reported trends seemingly based on similar statistical methods and databases. Differences arise from the variety of techniques used to reduce nontrend variation in time series, including mitigating the effects of meteorology and the variety of metrics used to track changes. In this paper, we review the trend assessment techniques being used in the air pollution field and discuss their strengths and limitations in discerning and attributing changes in O₃ to emission control policies.     

Parameter Evaluation and Model Validation of Ozone Exposure Assessment Using Harvard Southern California Chronic Ozone Exposure Study Data

Abstract

To examine factors influencing long‐term ozone (O₃) exposures by children living in urban communities, the authors analyzed longitudinal data on personal, indoor, and outdoor O₃ concentrations, as well as related housing and other questionnaire information collected in the one‐year‐long Harvard Southern California Chronic Ozone Exposure Study. Of 224 children contained in the original data set, 160 children were found to have longitudinal measurements of O₃ concentrations in at least six months of 12 months of the study period. Data for these children were randomly split into two equal sets: one for model development and the other for model validation. Mixed models with various variance‐covariance structures were developed to evaluate statistically important predictors for chronic personal ozone exposures. Model predictions were then validated against the field measurements using an empirical best‐linear unbiased prediction technique.The results of model fitting showed that the most important predictors for personal ozone exposure include indoor O₃ concentration, central ambient O₃ concentration, outdoor O₃ concentration, season, gender, outdoor time, house fan usage, and the presence of a gas range in the house. Hierarchical models of personal O₃ concentrations indicate the following levels of explanatory power for each of the predictive models: indoor and outdoor O₃ concentrations plus questionnaire variables, central and indoor O₃ concentrations plus questionnaire variables, indoor O₃ concentrations plus questionnaire variables, central O₃ concentrations plus questionnaire variables, and questionnaire data alone on time activity and housing characteristics. These results provide important information on key predictors of chronic human exposures to ambient O₃ for children and offer insights into how to reliably and cost‐effectively predict personal O₃ exposures in the future. Furthermore, the techniques and findings derived from this study also have strong implications for selecting the most reliable and cost‐effective exposure study design and modeling approaches for other ambient pollutants, such as fine particulate matter and selected urban air toxics.     

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.     

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.     

Air pollution forecast in cities by an air pollution index highly correlated with meteorological variables

There are many different air pollution indexes which represent the global urban air pollution situation. The daily index studied here is also highly correlated with meteorological variables and this index is capable of identifying those variables that significantly affect the air pollution. The index is connected with attention levels of NO₂, CO and O₃ concentrations. The attention levels are fixed by a law proposed by the Italian Ministries of Health and Environment. The relation of that index with some meteorological variables is analysed by the linear multiple partial correlation statistical method. Florence, Milan and Vicence were selected to show the correlation among the air pollution index and the daily thermic excursion, the previous day's air pollution index and the wind speed. During the January–March period the correlation coefficient reaches 0.85 at Milan. The deterministic methods of forecasting air pollution concentrations show very high evaluation errors and are applied on limited areas around the observation stations, as opposed to the whole urban areas. The global air pollution, instead of the concentrations at specific observation stations, allows the evaluation of the level of the sanitary risk regarding the whole urban population.     

Variation of surface ozone in Campo Grande,Brazil: meteorological effect analysis and prediction

The effect of meteorological variables on surface ozone (O₃) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O₃ concentration, relative humidity and solar radiation. The threshold model that considers two O₃ regimes was the one that correctly describes the effect of important meteorological variables in O₃ behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O₃ regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O₃ concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O₃ behaviour, being useful to define policy strategies for human health protection regarding air pollution.     

Assessment of nitrogen oxides and ground-level ozone behavior in a dense air quality station network: Case study in the Lesser Antilles Arc

ABSTRACT

This paper presents a study on ground-level ozone (O₃), nitrogen oxides (NO_x = NO + NO₂) concentrations, and their variabilities in the ambient air of three sites of a tropical archipelago that is moderately urbanized. Statistical analysis was performed on a quite complete (>80%) set of 5 years of measurements (2008–2012). There are few studies on those pollutants and their seasonal behavior in the Caribbean area, where pollution level and cities configuration are different from megacities. Analyses are focused on pollutant variations at the scale of the day, the week, and the seasons, using hourly data. The observations show that NO_x concentrations are more elevated during the wet season, whereas O₃ concentrations are higher in the dry season. Amplitudes of ozone cycles are strongly influenced by meteorological conditions (temperature, global radiation, and wind speed) and prevailing levels of NO_x. An ozone weekend effect is detected with the highest amplitude in the city, where anthropogenic activity is the lowest during the weekend. Due to the nature and the origin of pollutants, NO_x shows higher variability than O₃ in the time series. Our results evince the need for continuous measurements of volatile organic compounds (VOCs) in order to better quantify their contribution in O₃ formation in an insular context where numerous natural sources have been identified.

Implications: Statistical analyses of observed NO_x and O₃ concentrations for 5 years for a typical low industrialized site of the Caribbean area have been done. Air quality for those components is correct based on the standards of the World Health Orgaization, pollutant source spatial distributions, and level of industrialization. Observations show the same patterns as in megacities but also a strong impact of weather conditions and road traffic. Behaviors of O₃ cannot be fully explained without VOCs monitoring. Localization and type of AQS should be reconsidered to improve the accuracy of concentrations of the pollutant and better understand their behaviors.     

Meta-Analysis of Time-Series Studies of Air Pollution and Mortality: Effects of Gases and Particles and the Influence of Cause of Death,Age, and Season

Abstract

A comprehensive, systematic synthesis was conducted of daily time-series studies of air pollution and mortality from around the world. Estimates of effect sizes were extracted from 109 studies, from single- and multipollutant models, and by cause of death, age, and season. Random effects pooled estimates of excess all-cause mortality (single-pollutant models) associated with a change in pollutant concentration equal to the mean value among a representative group of cities were 2.0% (95% CI 1.5-2.4%) per 31.3 μg/m³ particulate matter (PM) of median diameter <10 μm (PM₁₀); 1.7% (1.2-2.2%) per 1.1 ppm CO; 2.8% (2.1-3.5%) per 24.0 ppb NO₂; 1.6% (1.1-2.0%) per 31.2 ppb O₃; and 0.9% (0.7-1.2%) per 9.4 ppb SO₂ (daily maximum concentration for O₃, daily average for others). Effect sizes were generally reduced in multipollutant models, but remained significantly different from zero for PM₁₀ and SO₂. Larger effect sizes were observed for respiratory mortality for all pollutants except O₃. Heterogeneity among studies was partially accounted for by differences in variability of pollutant concentrations, and results were robust to alternative approaches to selecting estimates from the pool of available candidates. This synthesis leaves little doubt that acute air pollution exposure is a significant contributor to mortality.     

An Evaluation of the Kriging Method to Predict 7-h Seasonal Mean Ozone Concentrations for Estimating Crop Losses

Using kriging, a statistical technique, the National Crop Loss Assessment Network (NCLAN) program estimated growing season 5-month (May-September) ambient 7-h mean O₃ concentrations for each of the major crop growing areas of the United States for 1978-1982. The O₃ estimates were used to predict economic benefits anticipated by lowering O₃ levels in the United States. This paper reviews NCLAN’s use of kriging to estimate 7-h seasonal mean O₃ concentrations for crop growing regions. Although the original kriging program used by NCLAN incorrectly calculated the diagonal elements of the kriging equations, this omission did not result in significant errors in the predicted estimates. Most of the data used in estimating the 7-h seasonal values were obtained from urban areas; the use of these data tended to underestimate the 7-h seasonal O₃ concentrations in rural areas. It is recommended that only O₃ data that are representative of agricultural areas and have been collected under accepted quality assurance programs be used In future kriging efforts.     

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.     

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.     

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.     

Effects of Land Use Data on Dry Deposition in a Regional Photochemical Model for Eastern Texas

ABSTRACT

Land use data are among the inputs used to determine dry deposition velocities for photochemical grid models such as the Comprehensive Air Quality Model with extensions (CAMx) that is currently used for attainment demonstrations and air quality planning by the state of Texas. The sensitivity of dry deposition and O₃ mixing ratios to land use classification was investigated by comparing predictions based on default U.S. Geological Survey (USGS) land use data to predictions based on recently compiled land use data that were collected to improve biogenic emissions estimates. Dry deposition of O₃ decreased throughout much of eastern Texas, especially in urban areas, with the new land use data. Predicted 1-hr averaged O₃ mixing ratios with the new land use data were as much as 11 ppbv greater and 6 ppbv less than predictions based on USGS land use data during the late afternoon. In addition, the area with peak O₃ mixing ratios in excess of 100 ppbv increased significantly in urban areas when deposition velocities were calculated based on the new land use data. Finally, more detailed data on land use within urban areas resulted in peak changes in O₃ mixing ratios of ~2 ppbv. These results indicate the importance of establishing accurate, internally consistent land use data for photochemical modeling in urban areas in Texas. They also indicate the need for field validation of deposition rates in areas experiencing changing land use patterns, such as during urban reforestation programs or residential and commercial development.     

Effect of Hydrocarbon and NO x on Photochemical Smog Formation under Simulated Transport Conditions

The body of information presented in this paper is directed to those individuals concerned with the effect of urban pollution on downwind areas. Concern has been expressed over the appropriate hydrocarbon and NO _x control strategy to be used in minimizing the effects of ozone and NO₂ on urban population centers and their downwind environs. O₃ and NO₂ formation were studied in smog chamber irradiations as a function of the initial NO _x concentration at three hydrocarbon concentrations. By carrying out the irradiations for a period of time equivalent to one solar day in a continuously diluting system, smog formation in a chemically reacting pollutant system under transport was simulated. The results of this experimental simulation suggest that hydrocarbon reduction reduces O₃ in urban as well as downwind areas while NO _x reduction increases O₃ in the urban area and has little effect on O₃ in downwind areas. Both hydrocarbon and NO _x reduction will reduce atmospheric NO₂ levels, with the effect of NO _x reduction generally being more pronounced.     

Neural Network Model for Predicting Peak Photochemical Pollutant Levels

ABSTRACT

In this paper, an attempt is made for the 24-hr prediction of photochemical pollutant levels using a neural network model. For this purpose, a model is developed that relates peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O₃ and NO₂ from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows a good agreement. In addition, a series of sensitivity tests is performed in order to evaluate the sensitivity of the model to the uncertainty in meteorological variables. Model forecasts are generally rather insensitive to small perturbations in most of the input meteorological data, while they are relatively more sensitive in changes in wind speed and direction.     

A Study of the Association between Daily Mortality and Ambient Air Pollutant Concentrations in Pittsburgh,Pennsylvania

ABSTRACT

We have studied the possible association of daily mortality with ambient pollutant concentrations (PM₁₀, CO, O₃, SO₂, NO₂, and fine [PM_{2 5}] and coarse PM) and weather variables (temperature and dew point) in the Pittsburgh, PA, area for two age groups—less than 75, and 75 and over—for the 3-year period of 1989-1991. Correlation functions among pollutant concentrations show important seasonal dependence, and this fact necessitates the use of seasonal models to better identify the link between ambient pollutant concentrations and daily mortality. An analysis of the seasonal model results for the younger-age group reveals significant multicollinearity problems among the highly correlated concentrations of PM₁₀, CO, and NO₂ (and O₃ in spring and summer), and calls into question the rather consistent results of the single- and multi-pollutant non-seasonal models that show a significant positive association between PM₁₀ and daily mortality. For the older-age group, dew point consistently shows a significant association with daily mortality in all models. Collinearity problems appear in the multi-pollutant seasonal and non-seasonal models such that a significant, positive PM₁₀ coefficient is accompanied by a significant, negative coefficient of another ambient pollutant, and the identity of this other pollutant changes with season. The PM₂₅ data set is half that of PM₁₀. Identical-model runs for both data sets reveal instability in the pollutant coefficients, especially for the younger age group. The concern for the instability of the pollutant coefficients due to a small signal-to-noise ratio makes it impossible to ascertain credibly the relative associations of the fine- and coarse-particle modes with daily mortality. In this connection, we call for caution in the interpretation of model results for causal inference when the models use fully or partially estimated PM values to fill large data gaps.     

Identification of redundant air quality measurements through the use of principal component analysis

This study aims to show how principal component analysis (PCA) can be used to identify redundant measurements in air quality monitoring networks. The minimum number of air quality monitoring sites in Oporto Metropolitan Area (Oporto-MA) was evaluated using PCA and then compared to the one settled by the legislation. Nine sites, monitoring NO₂, O₃ and PM₁₀, were selected and the air pollutant concentrations were analysed from January 2003 to December 2005. PCA was applied to the data corresponding to the first two years that were divided into annual quarters to verify the persistence of the PCA results. The number of principal components (PCs) was selected by applying two criteria: Kaiser (PCs with eigenvalues greater than 1) and ODV₉₀ (PCs representing at least 90% of the original data variance). Each pollutant was analysed separately. The two criteria led to different results. Using Kaiser criterion for the eight analysed periods, two PCs were selected in: (i) five periods for O₃ and PM₁₀; and (ii) six periods for NO₂. These PCs had important contributions of the same groups of monitoring sites. The percentage of the original data variance contained in the selected PCs using this criterion was always below 90%. Thus, the results obtained using ODV₉₀ were considered with more confidence. Using this criterion, only five monitoring sites for NO₂, three for O₃ and seven for PM₁₀ were needed to characterize the region. The number of monitoring sites for NO₂ and O₃ was in agreement with what was established by the legislation. However, for PM₁₀, Oporto-MA needed two more monitoring sites. To validate PCA results, statistical models were determined to estimate air pollutant concentrations at removed monitoring sites using the concentrations measured at the remaining monitoring sites. These models were applied to a year's data. The good performance obtained by the models showed that the monitoring sites selected by the procedure presented in this study were enough to infer the air pollutant concentrations in the region defined by the initial monitoring sites. Additionally, the air pollutant analysers corresponding to the redundant measurements can be installed in non-monitored regions, allowing the enlargement of the air quality monitoring network.     

Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo,Italy

Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO₂, O₃, PM10, NO₂, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between 1 January 2003 to 31 December 2004 in eight monitoring stations in urban area of Palermo (Italy). Experimental trials show that the developed and tuned model is appropriate, giving small values of root mean square error (RMSE) , mean absolute error (MAE) and mean square error (MSE). In addition, the related correlation coefficient ranges from 0.72 to 0.97 for each forecasted pollutant, underlying a small difference between the forecasted and the measured values. The above results make the proposed forecaster a powerful tool for pollution management systems.