Interactive design of urban level air quality monitoring network

An interactive optimization method for designing an air quality monitoring network in an urban area is proposed. The main purpose is to determine representative areas of monitoring stations rather than their precise locations. Two topologies are introduced to define similarities among pre-divided uniform meshes. The first is derived from the differences of long-term averages of pollutant concentrations between every two meshes and used in the Ward method clustering. The second is obtained by the cross-impacts between pairs of meshes and used as a constraint in the clustering process. Participation of specialists in the optimization process is allowed in such a way that they can modify the second topology by taking account of economic and physical conditions as well as inaccuracy of simulation models. This technique is applied to the NO_x monitoring network of Kyoto, Japan.     

Empirical Evaluation of Ambient Ozone Interpolation Procedures to Support Exposure Models

ABSTRACT

An intercomparison study has been performed with six empirical ozone interpolation procedures to predict hourly concentrations in ambient air between monitoring stations. The objective of the study is to use monitoring network data to empirically identify an improved procedure to estimate ozone concentrations at subject exposure points. Four of the procedures in the study are currently used in human exposure models (nearest monitors daily mean and maximum, regression estimate used in the U.S. Environmental Protection Agency's (EPA) pNEM, and inverse distance weighting), and two are being evaluated for this purpose (kriging in space and kriging in space and time). The study focused on spatial estimation during June 1-June 5, 1996, with relatively high observed ozone levels over Houston, Texas. The study evaluated these procedures at three types of locations with monitors of varying proximity. Results from the empirical evaluation indicate that kriging in space and time provides excellent estimates of ozone concentrations within a monitoring network, while the more often used techniques failed to capture observed pollutant concentrations. Improved estimation of pollutant concentrations within the region, and thus at subject locations, should result in improved exposure modeling.     

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

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

Optimal reduction of the ozone monitoring network over France

Ozone is a harmful air pollutant at ground level, and its concentrations are measured with routine monitoring networks. Due to the heterogeneous nature of ozone fields, the spatial distribution of the ozone concentration measurements is very important. Therefore, the evaluation of distributed monitoring networks is of both theoretical and practical interests. In this study, we assess the efficiency of the ozone monitoring network over France (BDQA) by investigating a network reduction problem. We examine how well a subset of the BDQA network can represent the full network. The performance of a subnetwork is taken to be the root mean square error (rmse) of the hourly ozone mean concentration estimations over the whole network given the observations from that subnetwork. Spatial interpolations are conducted for the ozone estimation taking into account the spatial correlations. Several interpolation methods, namely ordinary kriging, simple kriging, kriging about the means, and consistent kriging about the means, are compared for a reliable estimation. Exponential models are employed for the spatial correlations. It is found that the statistical information about the means improves significantly the kriging results, and that it is necessary to consider the correlation model to be hourly-varying and daily stationary. The network reduction problem is solved using a simulated annealing algorithm. Significant improvements can be obtained through these optimizations. For instance, removing optimally half the stations leads to an estimation error of the order of the standard observational error (10 μg m^?3). The resulting optimal subnetworks are dense in urban agglomerations around Paris (Île-de-France) and Nice (Côte d’Azur), where high ozone concentrations and strong heterogeneity are observed. The optimal subnetworks are probably dense near frontiers because beyond these frontiers there is no observation to reduce the uncertainty of the ozone field. For large rural regions, the stations are uniformly distributed. The fractions between urban, suburban and rural stations are rather constant for optimal subnetworks of larger size (beyond 100 stations). By contrast, for smaller subnetworks, the urban stations dominate.     

Modeling traffic air pollution in street canyons in New York City for intra-urban exposure assessment in the US Multi-Ethnic Study of atherosclerosis and air pollution

We evaluated the Danish AirGIS air quality and exposure model system using air quality measurement data from New York City in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Measurements were used from three US EPA Air Quality System (AQS) monitoring stations and a comprehensive MESA Air measurement campaign including about 150 different locations and about 650 samples of about 2 week measurements of NO_x, NO₂ and PM_2.5. AirGIS is a deterministic exposure model system based on the dispersion models Operational Street Pollution Model (OSPM) and the Urban Background Model (UBM). The UBM model reproduced the annual levels within 1–26% depending on station and pollutant at the three urban background EPA monitor stations, and generally reproduced well the seasonal and diurnal variation. The full model with OSPM and UBM reproduced the MESA Air measurements with a correlation coefficient of r² = 0.51 for NO_x, r² = 0.28 for NO₂ and r² = 0.73 for PM_2.5.     

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in “land use” regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM_2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.     

A methodology to urban air quality assessment during large time periods of winter using computational fluid dynamic models

The representativeness of point measurements in urban areas is limited due to the strong heterogeneity of the atmospheric flows in cities. To get information on air quality in the gaps between measurement points, and have a 3D field of pollutant concentration, Computational Fluid Dynamic (CFD) models can be used. However, unsteady simulations during time periods of the order of months, often required for regulatory purposes, are not possible for computational reasons. The main objective of this study is to develop a methodology to evaluate the air quality in a real urban area during large time periods by means of steady CFD simulations. One steady simulation for each inlet wind direction was performed and factors like the number of cars inside each street, the length of streets and the wind speed and direction were taken into account to compute the pollutant concentration. This approach is only valid in winter time when the pollutant concentrations are less affected by atmospheric chemistry. A model based on the steady-state Reynolds-Averaged Navier–Stokes equations (RANS) and standard k-? turbulence model was used to simulate a set of 16 different inlet wind directions over a real urban area (downtown Pamplona, Spain). The temporal series of NO_x and PM₁₀ and the spatial differences in pollutant concentration of NO₂ and BTEX obtained were in agreement with experimental data. Inside urban canopy, an important influence of urban boundary layer dynamics on the pollutant concentration patterns was observed. Large concentration differences between different zones of the same square were found. This showed that concentration levels measured by an automatic monitoring station depend on its location in the street or square, and a modelling methodology like this is useful to complement the experimental information. On the other hand, this methodology can also be applied to evaluate abatement strategies by redistributing traffic emissions.     

The National Ambient Air Monitoring Strategy: Rethinking the Role of National Networks

Abstract

A current re-engineering of the United States routine ambient monitoring networks intended to improve the balance in addressing both regulatory and scientific objectives is addressed in this paper. Key attributes of these network modifications include the addition of collocated instruments to produce multiple pollutant characterizations across a range of representative urban and rural locations in a new network referred to as the National Core Monitoring Network (NCore). The NCore parameters include carbon monoxide (CO), sulfur dioxide (SO₂), reactive nitrogen (NO_y), ozone (O₃), and ammonia (NH₃) gases and the major fine particulate matter (PM_2.5) aerosol components (ions, elemental and organic carbon fractions, and trace metals). The addition of trace gas instruments, deployed at existing chemical speciation sites and designed to capture concentrations well below levels of national air quality standards, is intended to support both long-term epidemiological studies and regional-scale air quality model evaluation. In addition to designing the multiple pollutant NCore network, steps were taken to assess the current networks on the basis of spatial coverage and redundancy criteria, and mechanisms were developed to facilitate incorporation of continuously operating particulate matter instruments.     

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 Survey Technique for Determining The Representativeness of Urban Air Monitoring Stations With Respect to Carbon Monoxide

An air quality survey technique for measuring the horizontal spatial variation of carbon monoxide concentrations in urban areas is described; it was used to determine how representative an urban air monitoring station is of concentrations throughout the city.

The survey technique was applied in San Jose, Calif., where 1128 samples were collected over a six-month period and were compared with the values recorded simultaneously at the urban air monitoring station. All samples were collected at “breathing height” within a 13-square-mile grid which included the downtown area as well as surrounding residential and industrial locations. Three basic sampling strategies were employed to answer specific questions about the distribution of carbon monoxide concentration: (7) walking sampling, in which samples were obtained while walking along the sidewalks of congested downtown streets, (2) random spatial sampling, in which samples were collected at randomly selected points in the urban grid, and (3) specialized sampling in the immediate vicinity of the air monitoring station.

The results indicate that pedestrians on downtown streets in San Jose can be exposed to concentrations above the federal air quality standards without these values being observed at the air monitoring station. There also is evidence that, at any instant of time, similar values of carbon monoxide exist throughout this city (within a 13-square mile area), provided that measurements are not made in close proximity to streets. Furthermore, the higher concentrations observed in the immediate vicinity of streets decrease quite rapidly with increasing horizontal distance from these streets.

These findings, in the view of the authors, raise serious doubts as to whether it is possible to determine if air quality standards as currently defined are actually being met in urban areas using data from present-day air monitoring stations.     

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.     

Evaluation of air quality zone classification methods based on ambient air concentration exposure

Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure. The purpose of this paper is to compare the risk associated with selecting one ambient air classification approach over another by testing the possible exposure an individual living within a zone may face. The chronic daily intake (CDI) is utilized to compare different pollutant exposures over the classification duration of 3 years between two classification methods. Historical data collected from air monitoring stations in Kuwait are used to build representative models of 1-hr NO₂ and 8-hr O₃ within a zone that meets the compliance requirements of each method. The first method, the “3 Strike” method, is a conservative approach based on a winner-take-all approach common with most compliance classification methods, while the second, the 99% Rule method, allows for more robust analyses and incorporates long-term trends. A Monte Carlo analysis is used to model the CDI for each pollutant and each method with the zone at a single station and with multiple stations. The model assumes that the zone is already in compliance with air quality standards over the 3 years under the different classification methodologies. The model shows that while the CDI of the two methods differs by 2.7% over the exposure period for the single station case, the large number of samples taken over the duration period impacts the sensitivity of the statistical tests, causing the null hypothesis to fail. Local air quality managers can use either methodology to classify the compliance of an air zone, but must accept that the 99% Rule method may cause exposures that are statistically more significant than the 3 Strike method.

Implications: A novel method using the Central Limit Theorem and Monte Carlo analysis is used to directly compare different air standard compliance classification methods by estimating the chronic daily intake of pollutants. This method allows air quality managers to rapidly see how individual classification methods may impact individual population groups, as well as to evaluate different pollutants based on dosage and exposure when complete health impacts are not known.     

Assessing seasonal and spatial trends of persistent organic pollutants (POPs) in Indian agricultural regions using PUF disk passive air samplers

The first survey of persistent organic pollutant (POP) concentrations in air across several Indian agricultural regions was conducted in 2006-2007. Passive samplers comprising polyurethane foam (PUF) disks were deployed on a quarterly basis at seven stations in agricultural regions, one urban site and one background site. The project was conducted as a sub-project of the Global Atmospheric Passive Sampling (GAPS) Network. In addition to revealing new information on air concentrations of several organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs), the study has demonstrated the feasibility of conducting regional-scale monitoring for POPs in India using PUF disk samplers. The following analytes were detected with relatively high concentrations in air (mean for 2006 and 2007, pg/m³): α- and γ-hexachlorocyclohexane (HCH) (292 and 812, respectively); endosulfan I and II (2770 and 902, respectively); p,p′-DDE and p,p′-DDT (247 and 931, respectively); and for the sum of 48 PCBs, 12,100 (including a site with extremely high air concentrations in 2007) and 972 (when excluding data for this site).     

Statistical and diagnostic evaluation of a new-generation urban dispersion modelling system against an extensive dataset in the Helsinki area

We have developed a modelling system for predicting the traffic volumes, emissions from stationary and vehicular sources, and atmospheric dispersion of pollution in an urban area. This paper describes a comparison of the NO_x and NO₂ concentrations predicted using this modelling system with the results of an urban air quality monitoring network. We performed a statistical analysis to determine the agreement between predicted and measured hourly time series of concentrations at four permanently located and three mobile monitoring stations in the Helsinki Metropolitan Area in 1996–1997 (at a total of ten urban and suburban measurement locations). At the stations considered, the so-called index of agreement values of the predicted and measured time series of the NO₂ concentrations vary between 0.65 and 0.82, while the fractional bias values range from −0.29 to +0.26. In comparison with corresponding results presented in the literature, the agreement between the measured and predicted datasets is good, as indicated by these statistical parameters. The seasonal variations of the NO₂ concentrations were analysed in terms of the relevant meteorological parameters. We also analysed the difference between model predictions and measured data diagnostically, in terms of meteorological parameters, including wind speed and direction (the latter separately for two wind speed classes), atmospheric stability and ambient temperature, at two monitoring stations in central Helsinki. The modelling system tends to overpredict the measured NO₂ concentrations both at the highest (u⩾6 m s⁻¹) and at the lowest wind speeds (u<2 m s⁻¹). For higher wind speeds, the modelling system overpredicts the measured NO₂ concentrations in certain wind direction intervals; specific ranges were found for both monitoring stations considered. The modelling system tends to underpredict the measured concentrations in convective atmospheric conditions, and overpredict in stable conditions. The possible physico-chemical reasons for these differences are discussed.     

Regulation of Indirect Sources

The Division of Air Pollution Control, Illinois Environmental Protection Agency, has conducted an ambient air quality monitoring project focusing on carbon monoxide levels in and around several indirect sources. An analysis of the data indicates that highway-type pollutant emissions have the greatest impact on receptors in the vicinity of indirect sources. This implies that the principal, localized constraint on the siting of indirect sources will be the carbon monoxide generated on public roadways servicing those indirect sources. Clearly, adequate procedures must be developed to link such highway-type emissions to pollutant concentrations. An area-source model and a line-source model were tested using the data generated during the monitoring project. Favorable results were achieved using the line-source model. The proper siting of indirect sources involves the allocation of roadway capacity by the governmental units responsible for transportation network design, working in conjunction with regional planning bodies. A regulatory structure is suggested which emphasizes a regional approach, and an example of an air quality allocation scheme is given. The methodology is applicable to all automotive air pollutants although, in general, localized sensitivity is lost for N0₂ and photochemical oxi-dants.     

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.     

Absorption and fluorescence properties of chromophoric dissolved organic matter: implications for the monitoring of water quality in a large subtropical reservoir

The development of techniques for real-time monitoring of water quality is of great importance for effectively managing inland water resources. In this study, we first analyzed the absorption and fluorescence properties in a large subtropical reservoir and then used a chromophoric dissolved organic matter (CDOM) fluorescence monitoring sensor to predict several water quality parameters including the total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), dissolved organic carbon (DOC), and CDOM fluorescence parallel factor analysis (PARAFAC) components in the reservoir. The CDOM absorption coefficient at 254 nm (a(254)), the humic-like component (C1), and the tryptophan-like component (C3) decreased significantly along a gradient from the northwest to the lake center, northeast, southwest, and southeast region in the reservoir. However, no significant spatial difference was found for the tyrosine-like component (C2), which contributed only four marked peaks. A highly significant linear correlation was found between the a(254) and CDOM concentration measured using the CDOM fluorescence sensor (r ²?=?0.865, n?=?76, p?相似文献   

Characterizing the spatiotemporal variability of PM2.5 in Cusco,Peru using kriging with external drift

Advancing the understanding of spatiotemporal aspects of air pollution in the urban environment is an area where improved methods can be of great benefit to exposure assessment and policy support. This paper explores the potential of a technique known as kriging with external drift (KED) to provide high resolution maps of fine particulate matter for a downtown region of Cusco, Peru. There were three stages in this research. The first was to conduct a pilot level monitoring campaign to investigate ambient, regional, and street-level air pollutant concentrations for particulate matter (PM_2.5, PM₁₀) and carbon monoxide (CO) in the Province of Cusco. The second was to compile observations within a geographic information system (GIS) in order to characterize the proximal effect of the local transportation network, elevation, and land use classifications on PM_2.5. Third, regression, ordinary kriging and kriging with external drift were used to model PM_2.5 for three select time periods during a 24-h day. Statistical evaluations indicate kriging with external drift resulted in the strongest models explaining 64% of variability seen with morning particle concentrations, 25% for afternoon particles, and 53% in evening particles. These models capture spatial and temporal variability for air pollution in Cusco. These variations seem to be influenced, to varying degrees, by elevation, meteorological conditions, spatial location, and transportation characteristics. In conclusion, combining GIS, meteorological data and geostatistics proved to be a complementary suite of tools for incorporating spatiotemporal analysis into the air quality assessment.     

Prediction of carbon monoxide acute air pollution episodes. Model formulation and first application in Lombardy

This paper describes a method to forecast the exceedance probability of a fixed threshold for a certain air pollutant concentration. This general approach has been applied in the case of carbon monoxide on 35 traffic monitoring stations in the Lombardy Region. The implemented model has been called FOREPOLL (Forecast Pollution).The model structure, consisting in three basic modules (the deterministic, the stochastic and the Bayesian one), has been thought to be adjustable to different stations of an air quality network and to various pollutants. Forepoll uses the last biennium data set of the station in exam (pollutants and micrometeorological parameters) as a moving learning time and also needs the forecasted synoptic weather type. In the first module the daily maxima of the hourly measured pollutant concentrations are checked in order to eliminate some known physical dependencies, such as the temperature dependence of the emissions. Then the data are divided into subgroups depending on the weather type and in each group fitted by a different Weibull distribution. To provide a first a priori exceedance probability, this distribution is daily rebuilt by taking into account the forecasted parameters. In the last module this probability is enhanced or reduced using simple, experience based, bayesian rules providing the a posteriori exceedance probability.Model validation trials have been carried out on a year of CO forecasted concentrations in different air quality stations; the first results are quite good particularly for the metropolitan areas, because the model seems to work better in case of stronger and more diffuse pollution.     

Long-term trends in airborne SO2 in an air quality monitoring station in Seoul,Korea, from 1987 to 2013

Atmospheric concentration of sulfur dioxide (SO₂) was intermittently measured at an air quality monitoring (AQM) station in the Yong-san district of Seoul, Korea, between 1987 and 2013. The SO₂ level was compared with other important pollutants concurrently measured, including methane (CH₄), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO₂), ozone (O₃), and particulate matter (PM₁₀). If split into three different periods (period 1, 1987–1988, period 2, 1999–2000, and period 3, 2004–2013), the respective mean [SO₂] values (6.57 ± 4.29, 6.30 ± 2.44, and 5.29 ± 0.63 ppb) showed a slight reduction across the entire study period. The concentrations of SO₂ are found to be strongly correlated with other pollutants such as CO (r = 0.614, p = 0.02), which tracked reductions in reported emissions due to tighter emissions standards enacted by the South Korean government. There was also a clear seasonal trend in the SO₂ level, especially in periods 2 and 3, reflecting the combined effects of domestic heating by coal briquettes and meteorological conditions. Although only a 16% concentration reduction was achieved during the 27-year study duration, this is significant if one considers rapid urbanization, an 83.2% increase in population, and rapid industrialization that took place during that period.

Implications: Since 1970, a network of air quality monitoring (AQM) stations has been operated by the Korean Ministry of Environment (KMOE) for routine nationwide monitoring of air pollutant concentrations in urban/suburban areas. To date, the information obtained from these stations has provided a platform for analyzing long-term trends of major pollutant species. In this study, we examined the long-term trends of SO₂ levels and relevant environmental parameters monitored continuously in the Yong-san district of Seoul between 1987 and 2013. The data were analyzed over various time scales (i.e., monthly, seasonal, and annual intervals). The results obtained from this study will allow us to assess the effectiveness of abatement strategy and to predict future concentrations trends in association with future abatement strategies and technologies.