Episodes Of Poor Air Quality Research Articles

Investigation into the Use of Satellite Data in Aiding Characterization of Particulate Air Quality in the Atlanta, Georgia Metropolitan Area

ABSTRACT Poor air quality episodes occur often in metropolitan Atlanta, GA. The primary focus of this research is to assess the capability of satellites as a tool in characterizing air quality in Atlanta. Results indicate that intracity PM2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) concentrations show similar patterns as other U.S. urban areas, with the highest concentrations occurring within the city. PM2.5 and MODIS (Moderate Resolution ImagingSpectroradiometer) aerosol optical depth (AOD) have higher values in the summer than spring, yet MODIS AOD doubles in the summer unlike PM2.5. Most (80%) of the Ozone Monitoring Instrument aerosol index (AI) is below 0.5 with little differences between spring and summer. Using this value as a constraint of the carbonaceous aerosol signal in the urban area, aerosol transport events such as wildfire smoke associated with higher positive AI values can be identified. The results indicate that MODIS AOD is well correlated with PM2.5 on a yearly and seasonal basis with correlation coefficients as high as 0.8 for Terra and 0.7 for Aqua. A possible alternative view of the PM2.5 and AOD relationship is seen through the use of AOD thresholds. These probabilistic thresholds provide a means to describe the air quality index (AQI) through the use of multiyear AOD records for a specific area. The National Ambient Air Quality Standards (NAAQS) are used to classify the AOD into different AQI codes and probabilistically determine thresholds of AOD that represent most of a specific AQI category. For example, 80% of cases of moderate AQI days have AOD values between 0.5 and 0.6. The development of AOD thresholds provides a useful tool for evaluating air quality from the use of satellites in regions where there are sparse ground-based measurements of PM2.5. IMPLICATIONS Satellite aerosol products can be used successfully as a tool for characterizing air quality on an urban scale. Statistical analysis of multiyear satellite data can yield a useful and easily understandable way of describing air quality through satellite-derived AQI. In areas without many monitoring sites of PM2.5, this approach could be useful to those air quality forecasters. Additionally, the use of satellite data thresholds could increase satellite utility beyond that of qualifying events for exclusion using the U.S. Environmental Protection Agency's Exceptional Event Rule in determination of attainment of the NAAQS.

The sensitivity of OMI-derived nitrogen dioxide to boundary layer temperature inversions

We assess the sensitivity of tropospheric nitrogen dioxide (NO 2) derived from the Ozone Monitoring Instrument (OMI), to episodes of temperature inversion in the lower boundary layer. Vertical temperature data were obtained from a 91-m meteorological tower located in the study area, which is centered on the Hamilton Census Metropolitan Area, Ontario, Canada. Hamilton is an industrial city with high traffic volumes, and is therefore subjected to high levels of pollution. Pollution buildup is amplified by frequent temperature inversions which are commonly radiative, but are also induced by local physiography, proximity to Lake Ontario, and regional meteorology. The four-year period from January 2005 to December 2008 was investigated. Ground-level data for validation were obtained from in situ air quality monitors located in the study area. The results indicate that OMI is sensitive to changes in the NO 2 levels during temperature inversions, and exhibits changes which roughly parallel those of in situ monitors. Overall, an 11% increase in NO 2 was identified by OMI on inversion days, compared to a 44% increase measured by in situ monitors. The weekend effect was clearly exhibited under both normal and inversion scenarios with OMI. Seasonal and wind direction patterns also correlated fairly well with ground-level data. Temperature inversions have resulted in poor air quality episodes which have severely compromised the health of susceptible populations, sometime leading to premature death. The rationale for this study is to further assess the usefulness of OMI for population exposure studies in areas with sparse resources for ground-level monitoring.

Basin-scale wind transport during the MILAGRO field campaign and comparison to climatology using cluster analysis

Abstract. The MILAGRO field campaign was a multi-agency international collaborative project to evaluate the regional impacts of the Mexico City air pollution plume as a means of understanding urban impacts on the global climate. Mexico City lies on an elevated plateau with mountains on three sides and has complex mountain and surface-driven wind flows. This paper asks what the wind transport was in the basin during the field campaign and how representative it was of the climatology. Surface meteorology and air quality data, radiosondes and radar wind profiler data were collected at sites in the basin and its vicinity. Cluster analysis was used to identify the dominant wind patterns both during the campaign and within the past 10 years of operational data from the warm dry season. Our analysis shows that March 2006 was representative of typical flow patterns experienced in the basin. Six episode types were identified for the basin-scale circulation providing a way of interpreting atmospheric chemistry and particulate data collected during the campaign. Decoupling between surface winds and those aloft had a strong influence in leading to convection and poor air quality episodes. Hourly characterisation of wind circulation during the MILAGRO, MCMA-2003 and IMADA field campaigns enables the comparisons of similar air pollution episodes and the evaluation of the impact of wind transport on measurements of the atmospheric chemistry taking place in the basin.

Origins of chemical pollution derived from Mid-Atlantic aircraft profiles using a clustering technique

Upwind sources of NO x and SO 2 play a crucial role in the amount of O 3 and aerosols in the lower troposphere in the Mid-Atlantic US. This paper describes a novel method of clustering trace gas and aerosol profiles allowing for the quantification of the relationship between point sources and pollution levels. This improves our understanding of pollution origins and has the potential for prediction of episodes of poor air quality. A hierarchical clustering method was used to classify distinct chemical and meteorological events from over 200 aircraft vertical profiles in the lower troposphere. Profile measurements included O 3, SO 2, CO and particle scattering from June to August 1997–2003, in the Mid-Atlantic US (mostly in Maryland, Pennsylvania and Virginia). The clustering technique could discriminate distinct profile shapes including measurements made during the 2002 Canadian forest fires. Forty-eight-hour back trajectories were run for each profile and the integrated NO x and SO 2 point source emissions encountered by each trajectory were calculated using data from the EPA Clean Air Market Division's emissions database. There was a strong correlation between integrated NO x emissions and O 3 profiles, indicating that O 3 profiles are strongly influenced by and can be predicted with point source emissions. There is a prevalent concentration of SO 2 over the eastern US with mixing ratios decreasing smoothly from about 3.5 ppb near the surface to 0.2 ppb at 2400 m.

A New Cost Function for Air Quality Modeling

In this paper a new Back-Propagation (BP) algorithm cost function is appropriately studied for the modeling of air pollution time series. The underlying idea is that of modifying the error definition in order to improve the capabilities of this kind of models to forecast episodes of poor air quality. The proposed error definition can be regarded as a generalization of the traditional squared error cost function thanks to the presence of a parameter ? which allows to obtain the ordinary BP as a special case when ??=?1. A criterion for choosing this parameter is stated based on setting a-priori a maximum level of allowable false alarms. The goodness of the proposed approach is assessed by means of case studies both on synthetic and measured air quality data.

An improved back propagation algorithm topredict episodes of poor air quality

This paper deals with the problem of modelling air pollution time series recorded at a given point by using a multi-layer perceptron based approach. In particular it is shown that the traditional back propagation (BP) algorithm, widely considered in the literature for this kind of application, can be improved to predict episodes of poor air quality. A novel version of the BP algorithm is developed and its performance is compared with that of the traditional algorithm in a case study which refers to the modelling of 1 h average maximum daily ozone concentration recorded in the industrial area of Melilli (Siracusa, Italy). The results obtained so far indicate that the proposed algorithm outperforms the traditional BP algorithm.

Heteroscedastic kernel ridge regression

In this paper we extend a form of kernel ridge regression (KRR) for data characterised by a heteroscedastic (i.e. input dependent variance) Gaussian noise process, introduced in Foxall et al. (in: Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002), Bruges, Belgium, April 2002, pp. 19–24). It is shown that the proposed heteroscedastic kernel ridge regression model can give a more accurate estimate of the conditional mean of the target distribution than conventional KRR and also provides an indication of the spread of the target distribution (i.e. predictive error bars). The leave-one-out cross-validation estimate of the conditional mean is used in fitting the model of the conditional variance in order to overcome the inherent bias in maximum likelihood estimates of the variance. The benefits of the proposed model are demonstrated on synthetic and real-world benchmark data sets and for the task of predicting episodes of poor air quality in an urban environment.

Maximum likelihood cost functions for neural network models of air quality data

The prediction of episodes of poor air quality using artiflcial neural networks is investigated, concentrating on selection of the most appropriate cost function used in training. Difierent cost functions correspond to difierent distributional assumptions regarding the data, the appropriate choice depends on whether a forecast of absolute pollutant concentration or prediction of exceedence events is of principle importance. The cost functions investigated correspond to logistic regression, homoscedastic Gaussian (i.e. conventional sum-of-squares) regression and heteroscedastic Gaussian regression. Both linear and non-linear neural network architectures are evaluated. While the results presented relate to a dataset describing the daily time-series of the concentration of surface level ozone (O3) in urban Berlin, the methods applied are quite general and applicable to a wide range of pollutants and locations. The heteroscedastic Gaussian regression model outperforms the other non-linear methods investigated, however there is little improvement resulting from the use of non-linear rather than linear models. Of greater signiflcance is the ∞exibility afiorded by the non-linear heteroscedastic Gaussian regression model for a range of potential endusers, who may all have difierent answers to the question: \What is more important, correctly predicting exceedences or avoiding false alarms?.

Social awareness of air quality information

In the UK, air quality information is made available through a range of media. However, limited attention has been paid to ensuring that the information is provided to the public in a format that is understandable and relevant to their needs. This research has begun the task of determining the nature and extent of public air quality requirements by performing a social survey (using a postal questionnaire) to provide a basic snapshot of the public’s views and by determining the views of information providers and interested professionals. The paper identifies the main shortcomings in current public air quality information provision. The social survey and workshop results demonstrate that current information provision and dissemination does not match public requirements; the depth and breadth of local information needs to be enhanced. Local authorities need to improve their co-ordination and collaboration, the role of the mass-media needs to be considered carefully, air quality needs to be better described and its implications for individuals spelled out and certain public groups need special consideration. In addition, local authorities need more guidance on communicating air quality, possibly through a best practice guide. Further research is required to identify the best descriptors for air quality, to improve the effectiveness of public advice during episodes of poor air quality and to use public air quality information to effect behavioral changes.

The introduction of local air quality management in the United Kingdom: A review and theoretical framework

In the light of recent episodes of poor air quality in many of the U.K.'s major urban areas, concern has been expressed regarding the apparent inability of existing air quality control procedures to effectively tackle contemporary scenarios. As a result of this, a new philosophy for air quality control has been sought which can provide a solid basis for the preservation and future improvement of air quality. It is proposed that a suitable mechanism for this would be found through the adoption of an integrated and holistic local air quality management approach. This paper will present and discuss a theoretical framework for the application of local air quality management in the U.K. and investigate the potential of the Environment Act (1995) to provide such a framework.

Effect of Ozone Inhalation on the Response to Nasal Challenge with Antigen of Allergic Subjects

The effect of oxidant inhalation on allergic illness is of interest because allergic patients often report increased respiratory symptoms during episodes of poor air quality, and epidemiologic studies demonstrate an association between increased levels of the air pollutant ozone and exacerbations of asthma. The purpose of this study was to characterize the upper respiratory response to ozone inhalation in asymptomatic, allergic subjects and to determine whether ozone pre-exposure increased the acute response to nasal challenge with antigen in these subjects. A group of 12 asymptomatic subjects with a history of allergic rhinitis were exposed in a randomized, cross-over design, at rest, on each of 2 days, separated by 2 wk, to 4 h of clean air or 0.5 ppm ozone in an environmental chamber. Following the exposure period, subjects underwent nasal challenge with four doses of antigen (1 to 1,000 PNU ragweed or grass). Symptoms were rated and nasal lavage performed after each dose. Measurement of histamine and albumin concentration and TAME-esterase activity and determination of cell counts and differentials were performed. Exposure to ozone caused significant increases in upper and lower respiratory symptoms, a mixed inflammatory cell influx with a sevenfold increase in naval lavage neutrophils, a 20-fold increase in eosinophils, and a tenfold increase in mononuclear cells, as well as an apparent sloughing of epithelial cells. There was a significant increase in nasal lavage albumin concentration on the ozone exposure day and a small increase in nasal lavage histamine concentration on both the ozone and clean air exposure days. TAME-esterase activity showed no significant increase overall, but increased at least twofold in 5 of 12 subjects. (ABSTRACT TRUNCATED AT 250 WORDS)