Variability of ozone air quality indicators in selected metropolitan statistical areas

This article analyzes numerical variability in ozone air quality data to understand how this variability affects the number of violations seen each year in metropolitan statistical areas (MSAs). Three commonly cited violation indices are used: 1) the annual number of expected exceedances averaged over 3 years is greater than 1; 2) the n+ 1th hourly value in n years of data is greater than 0.12 ppm; and 3) the annual number of expected exceedances is greater than 1. Only the first index is consistent with applicable regulations. The analyses indicate that about 23 percent of all MSAs with valid data had one or more change in their ozone violation status between 1979 and 1987. This change in status occurred for approximately 7 percent of all MSA-years of available data. This statistic was about one-third of the value usually obtained when the two incorrect, but commonly used, criteria of ozone violations are used.     

Evaluation of air quality models for the simulation of a high ozone episode in the Houston metropolitan area

A high ozone event in the Houston–Galveston–Brazoria area was utilized to study the shortcomings of the current air quality models. To improve the baseline simulations with the Comprehensive Air quality Model with Extensions (CAMx) for developing the state implementation plan, the Texas Commission on Environmental Quality (TCEQ) imputed emissions of highly reactive volatile organic compounds (HRVOCs) by scaling the amount of fugitive emissions of olefins to co-emitted NO_x from selected point sources, effectively multiplying by 3–12 times over the regular inventory values. In this paper, CAMx and the Community Multiscale Air Quality (CMAQ) model were used to determine if the imputed HRVOC emissions were consistent with the observed atmospheric conditions. With the base emissions, CMAQ and CAMx both with the Carbon-Bond 4 (CB-4) mechanism simulated similar ozone concentrations. But with the imputed HRVOC emissions, CMAQ predicted lower ozone peaks than CAMx in the vicinity and downwind of the Ship Channel and other highly HRVOC-rich areas. Based on analyses of sensitivity simulations of CMAQ with different emission inputs and vertical diffusion algorithms in the model, we found that the modeled atmosphere lacked reactivity to produce the observed high ozone event. Although the imputed HRVOC emissions improved ozone prediction at the surface sites, but the ethylene concentrations were not consistent with the measurements at the super sites (La Porte and Clinton) and by NOAA aircraft. Several sensitivity tests designed to provide additional radicals into the system and other research results suggested that the lack of reactivity may need to be corrected by targeted, and probably of episodic, increase of HRVOC emissions, from the sources in the Houston Ship Channel. Additional investigation of the ozone production efficiency for different chemical mechanisms is necessary to pinpoint the emissions uncertainty issues.     

Determining temporal and spatial variations in ozone air quality

Unless the change in emissions is substantial, the resulting improvement in ozone air quality can be easily masked by the meteorological variability. Therefore, the meteorological and chemical signals must be separated in examining ozone trends. In this paper, we discuss the use of the Kolmogorov-Zurbenko filter in evaluating the temporal and spatial variations in ozone air quality utilizing ozone concentration data from several monitoring locations in the northeastern United States. The results indicate a downward trend in the ozone concentrations during the period 1983-1992 at most locations in the northeastern United States. The results also reveal that ozone is a regional-scale problem in the Northeast.     

Monitoring of air pollution in Indian metropolitan cities: modelling and quality indexing

Air quality in cities is the result of a complex interaction between natural and anthropogenic environmental conditions. Delhi, as well as many other cities in India, is facing problems concerning air pollution. The increase in industrialisation and the vehicle fleet, poor control on emissions and little use of catalytic converters, produce a great amount of particulate and toxic gases. Data on air pollutants and meteorological variables were collected in the metropolitan cities Delhi, Kolkata, Mumbai and Chennai for the period July–August, 2001. Data were treated with the bivariate regression model to explore the influence of the meteorological variables on air pollutant concentrations, and were also used to compute an Air Quality Index, using the weighted arithmetic mean method. The proposed index seems to be applicable in the assessment of overall air quality with respect to air pollutants.     

Progress in developing an ANN model for air pollution index forecast

An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO₂ and NO_x, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM₁₀, SO₂, and NO₂. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.     

Vegetation exposure to ozone over the continental United States: Assessment of exposure indices by the Eta-CMAQ air quality forecast model

The main use of air quality forecast (AQF) models is to predict ozone (O₃) exceedances of the primary O₃ standard for informing the public of potential health concerns. This study presents the first evaluation of the performance of the Eta-CMAQ air quality forecast model to predict a variety of widely used seasonal mean and cumulative O₃ exposure indices associated with vegetation using the U.S. AIRNow O₃ observations. These exposure indices include two concentration-based O₃ indices, M7 and M12 (the seasonal means of daytime 7-h and 12-h O₃ concentrations, respectively), and three cumulative exposure-based indices, SUM06 (the sum of all hourly O₃ concentrations ≥ 0.06 ppm), W126 (hourly concentrations weighed by a sigmoidal weighting function), and AOT40 (O₃ concentrations accumulated over a threshold of 40 ppb during daylight hours). During a three-month simulation (July–September 2005), the model over predicted the M7 and M12 values by 8–9 ppb, or a NMB value of 19% and a NME value of 21%. The model predicts a central belt of high O₃ extending from Southern California to Middle Atlantic where the seasonal means, M7 and M12 (the seasonal means of daytime 7-h and 12-h O₃ concentrations), are higher than 50 ppbv. In contrast, the model is less capable of reproducing the observed cumulative indices. For AOT40, SUM06 and W126, the NMB and NME values are two- to three-fold of that for M7, M12 or peak 8-h O₃ concentrations. The AOT40 values range from 2 to 33 ppm h by the model and from 1 to 40 ppm h by the monitors. There is a significantly higher AOT40 value experienced in the United States in comparison to Europe. The domain-wide mean SUM06 value is 14.4 ppm h, which is about 30% higher than W126, and 40% higher than AOT40 calculated from the same 3-month hourly O₃ data. This suggests that SUM06 and W126 represent a more stringent standard than AOT40 if either the SUM06 or the W126 was used as a secondary O₃ standard. Although CMAQ considerably over predicts SUM06 and W126 values at the low end, the model under predicts the extreme high exposure values (>50 ppm h). Most of these extreme high values are found at inland California sites. Based on our analysis, further improvement of the model is needed to better capture cumulative exposure indices.     

Economic damages of ozone air pollution to crops using combined air quality and GIS modelling

This study aims at presenting a combined air quality and GIS modelling methodological approach in order to estimate crop damages from photochemical air pollution, depict their spatial resolution and assess the order of magnitude regarding the corresponding economic damages. The analysis is conducted within the Greater Thessaloniki Area, Greece, a Mediterranean territory which is characterised by high levels of photochemical air pollution and considerable agricultural activity. Ozone concentration fields for 2002 and for specific emission reduction scenarios for the year 2010 were estimated with the Ozone Fine Structure model in the area under consideration. Total economic damage to crops turns out to be significant and estimated to be approximately 43 M€ for the reference year. Production of cotton presents the highest economic loss, which is over 16 M€, followed by table tomato (9 M€), rice (4.2 M€), wheat (4 M€) and oilseed rape (2.8 M€) cultivations. Losses are not spread uniformly among farmers and the major losses occur in areas with valuable ozone-sensitive crops. The results are very useful for highlighting the magnitude of the total economic impacts of photochemical air pollution to the area’s agricultural sector and can potentially be used for comparison with studies worldwide. Furthermore, spatial analysis of the economic damage could be of importance for governmental authorities and decision makers since it provides an indicative insight, especially if the economic instruments such as financial incentives or state subsidies to farmers are considered.     

Use of an indoor air quality model (IAQM) to estimate indoor ozone levels

Currently, outdoor ozone levels in many U.S. cities exceed the primary health-based national ambient air quality standard. While outdoor ozone levels are an important measure of the severity of those exceedances, people typically spend more than 80 percent of their time indoors, where ozone levels are lower. Indoor ozone levels range from 10 to 80 percent of outdoor levels, with many people receiving a substantial portion of their ozone exposure while indoors. This paper uses an indoor air quality model (IAQM) to estimate indoor ozone levels by microenvironment type (home, office, and vehicle) and configuration (windows open, windows closed, older construction, weatherized, and air conditioned). The formulation of IAQM is discussed, along with specification of model parameters for ozone. The multicompartment version of IAQM is described, with a single-compartment version used for the analyses. IAQM-calculated ozone indoor-outdoor ratios compare well with research-reported values. Results indicate that ozone peak-concentration indoor-outdoor ratios range as follows: home--0.65 (windows open), 0.36 (air conditioned), 0.23 (typical construction, windows closed), and 0.05 (energy-efficient construction, windows closed); office--0.82 (heating, ventilation and air conditioning systems supplying 100 percent outdoor air), 0.60 (typical HVAC), and 0.32 (energy-efficient HVAC); and vehicle--0.41 (85 mph), 0.33 (55 mph), and 0.21 (10 mph). Analysis results are presented to characterize IAQM's sensitivity to assumed model parameters.     

Neural network-based study for predicting ground-level ozone concentration in large urban areas, applied to the Sao Paulo metropolitan area

Events of high concentration of ground-level ozone constitute a matter of major concern in large urban areas in terms of air quality, and public health. In the Sao Paulo Metropolitan Area (SPMA), air quality data generated by a network of air quality measuring stations have been used in a number of studies correlating ozone formation with different variables. A study was carried out on the application of neural network models in the identification of typical sceneries leading to high ground-level ozone concentrations in the SPMA. The results were then applied in the selection of variables, and in the definition of neural network-based models for estimating ozone levels from meteorological variables. When combined with existing weather prediction tools, the models can be applied in the prediction of ozone levels in the SPMA     

Long-term meteorologically independent trend analysis of ozone air quality at an urban site in the greater Houston area

The Houston-Galveston-Brazoria (HGB) area of Texas has a history of ozone exceedances and is currently classified under moderate nonattainment status for the 2008 8-hr ozone standard of 75 ppb. The HGB area is characterized by intense solar radiation, high temperature, and high humidity, which influence day-to-day variations in ozone concentrations. Long-term air quality trends independent of meteorological influence need to be constructed for ascertaining the effectiveness of air quality management in this area. The Kolmogorov-Zurbenko (KZ) filter technique, used to separate different scales of motion in a time series, is applied in the current study for maximum daily 8-hr (MDA8) ozone concentrations at an urban site (U.S. Environmental Protection Agency [EPA] Air Quality System [AQS] Site ID: 48-201-0024, Aldine) in the HGB area. This site, located within 10 miles of downtown Houston and the George Bush Intercontinental Airport, was selected for developing long-term meteorologically independent MDA8 ozone trends for the years 1990–2016. Results from this study indicate a consistent decrease in meteorologically independent MDA8 ozone between 2000 and 2016. This pattern could be partially attributed to a reduction in underlying nitrogen oxide (NO_x) emissions, particularly lowering nitrogen dioxide (NO₂) levels, and a decrease in the release of highly reactive volatile organic compounds (HRVOCs). Results also suggest solar radiation to be most strongly correlated to ozone, with temperature being the secondary meteorological control variable. Relative humidity and wind speed have tertiary influence at this site. This study observed that meteorological variability accounts for a high of 61% variability in baseline ozone (low-frequency component, sum of long-term and seasonal components), whereas 64% of the change in long-term MDA8 ozone post 2000 could be attributed to NO_x emission reduction. Long-term MDA8 ozone trend component was estimated to be decreasing at a linear rate of 0.412 ± 0.007 ppb/yr for the years 2000–2016 and 0.155 ± 0.005 ppb/yr for the overall period of 1990–2016.

Implications: The effectiveness of air emission controls can be evaluated by developing long-term air quality trends independent of meteorological influences. The KZ filter technique is a well-established method to separate an air quality time series into short-term, seasonal, and long-term components. This paper applies the KZ filter technique to MDA8 ozone data between 1990 and 2016 at an urban site in the greater Houston area and estimates the variance accounted for by the primary meteorological control variables. Estimates for linear trends of MDA8 ozone are calculated and underlying causes are investigated to provide a guidance for further investigation into air quality management of the greater Houston area.     

Evolution of air quality in the Sao Paulo metropolitan area and its relation with public policies

The Sao Paulo Metropolitan Area (SPMA) is one of the largest urban regions in the world, with more than 17 million inhabitants, about 2000 major industrial facilities, and more than 6 million vehicles based on diesel, gasoline, and ethanol. The area is thus a representative example of large urban region. The accumulated data collected by a network of air quality measuring stations, distributed throughout the SPMA, enables the monitoring of the air quality and is an adequate source of information for checking the effect of air quality control measures in the region. The present work shows that, although the levels of primary air pollutants have decreased over the last 20 years, events with high levels of NO₂, CO, particulate material and ozone still take place. In the last five years, ozone has become the most problematic pollutant, in view of the high frequency of peak events. Increased control of emission sources and adequate planning of the urban area, especially concerning the traffic system, are both necessary in order to keep pollution in the area under established levels.     

Application of an ISCST3 model for predicting urban air pollution in the Izmir metropolitan area

A Gaussian atmospheric dispersion model, Industrial Source Complex Short Term (ISCST3), was used to estimate ground-level concentrations of sulphur dioxide (SO₂) emitted from source categories of industrial and domestic heating in the city of Izmir, Turkey. Predictions were estimated for the year 2000 across a study area of 80 km x 100 km. Statistical analyses were carried out to evaluate the model performance by comparing predicted and observed SO₂ concentrations at four ambient air quality monitoring stations using two main methods root mean square error (RMSE) and an index of agreement (d). The results showed that industry was found as the most air-polluting sector and industries located at outside of the metropolitan area were found to carry important risks for urban air quality. The most polluted area was found at a distance of about 1 km from a major petroleum refinery and a large petrochemical industry.     

Air pollution and hospital admissions for diseases of the circulatory system in three U.S. metropolitan areas

Generalized additive models were used to analyze the time series of daily hospital admissions for cardiovascular and cerebrovascular diseases over the period of 1987-1995 in three major metropolitan areas--Cook County, IL; Los Angeles County, CA; and Maricopa County, AZ--in the United States. In Cook and Maricopa Counties, admissions information was only available for the elderly (ages 65 and over), while in Los Angeles County, admissions information was available for all ages. In Cook County, daily monitoring information was available on PM10, CO, SO2, NO2, and O3. In Los Angeles and Maricopa Counties, monitoring information was available daily on the gases, and information on PM10 was available every sixth day. In Los Angeles County, information on PM2.5 was also available every sixth day. In Cook and Los Angeles Counties, associations were found between each pollutant, with the exception of O3, and admissions for cardiovascular disease, with the gases showing the strongest associations. In two-pollutant models with PM and one of the gases, the effect of the gases remained stable, while the effect of PM became unstable and insignificant. In Maricopa County, the gases, with the exception of O3, were weakly associated with hospital admissions for cardiovascular disease, while PM was not. In two-pollutant models with two of CO, SO2, and NO2, the pattern of results is heterogeneous in the three counties. In all three counties, only weak evidence of any association between air pollution and cerebrovascular admissions was found.     

The global impact of ozone on agricultural crop yields under current and future air quality legislation

In this paper we evaluate the global impact of surface ozone on four types of agricultural crop. The study is based on modelled global hourly ozone fields for the year 2000 and 2030, using the global 1°×1° 2-way nested atmospheric chemical transport model (TM5). Projections for the year 2030 are based on the relatively optimistic “current legislation (CLE) scenario”, i.e. assuming that currently approved air quality legislation will be fully implemented by the year 2030, without a further development of new abatement policies. For both runs, the relative yield loss due to ozone damage is evaluated based on two different indices (accumulated concentration above a 40 ppbV threshold and seasonal mean daytime ozone concentration respectively) on a global, regional and national scale. The cumulative metric appears to be far less robust than the seasonal mean, while the seasonal mean shows satisfactory agreement with measurements in Europe, the US, China and Southern India and South-East Asia.Present day global relative yield losses are estimated to range between 7% and 12% for wheat, between 6% and 16% for soybean, between 3% and 4% for rice, and between 3% and 5% for maize (range resulting from different metrics used). Taking into account possible biases in our assessment, introduced through the global application of “western” crop exposure–response functions, and through model performance in reproducing ozone-exposure metrics, our estimates may be considered as being conservative.Under the 2030 CLE scenario, the global situation is expected to deteriorate mainly for wheat (additional 2–6% loss globally) and rice (additional 1–2% loss globally). India, for which no mitigation measures have been assumed by 2030, accounts for 50% of these global increase in crop yield loss. On a regional-scale, significant reductions in crop losses by CLE-2030 are only predicted in Europe (soybean) and China (wheat).Translating these assumed yield losses into total global economic damage for the four crops considered, using world market prices for the year 2000, we estimate an economic loss in the range $14–$26 billion. About 40% of this damage is occurring in China and India. Considering the recent upward trends in food prices, the ozone-induced damage to crops is expected to offset a significant portion of the GDP growth rate, especially in countries with an economy based on agricultural production.     

The effects of meteorology on ozone in urban areas and their use in assessing ozone trends

The United States Environmental Protection Agency issues periodic reports that describe air quality trends in the US. For some pollutants, such as ozone, both observed and meteorologically adjusted trends are displayed. This paper describes an improved statistical methodology for meteorologically adjusting ozone trends as well as characterizes the relationships between individual meteorological parameters and ozone. A generalized linear model that accommodates the nonlinear effects of the meteorological variables was fit to data collected for 39 major eastern US urban areas. Overall, the model performs very well, yielding R² statistics as high as 0.80. The analysis confirms that ozone is generally increasing with increasing temperature and decreasing with increasing relative humidity. Examination of the spatial gradients of these responses show that the effect of temperature on ozone is most pronounced in the north while the opposite is true of relative humidity. By including HYSPLIT-derived transport wind direction and distance in the model, it is shown that the largest incremental impact of wind direction on ozone occurs along the periphery of the study domain, which encompasses major NO_x emission sources.     

Impact of large scale circulation on European summer surface ozone and consequences for modelling forecast

In this study, we investigate the benefit for European ozone simulation of using day-to-day varying chemical boundary conditions produced by a global chemical weather forecast platform instead of climatological monthly means at the frontiers of a regional model. We performed two simulations over Europe using the regional (0.5 × 0.5°) CHIMERE CTM forced by global scale simulations based on the LMDz-INCA CTM. For summer 2005, ozone differences exceeding 20 ppb can be punctually found between these two simulations in the borders of the domain. The mean of the differences ranges between 0 and 3 ppb beyond 15° of the frontiers of the regional model.Correlations with ground-based ozone measurements at more than 400 stations are slightly increased by the use of daily boundary conditions. The simulation of the temporal variability is significantly enhanced in particular for the daily means and daily maxima. As expected, the gain is higher at the borders of the regional domain.The change of percentile distribution shows that the net impact of high temporal resolution boundary conditions is not of major concern for surface ozone peaks which are mainly due to local photochemistry. The use of daily boundary conditions is however necessary to correctly simulate concentrations in the 20–35 ppb range which are of crucial interest for human and vegetation exposure effects.     

Investigation into the use of satellite data in aiding characterization of particulate air quality in the Atlanta, Georgia metropolitan area

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 < or = 2.5 microm 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 Imaging Spectroradiometer) 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.     

Agricultural and green infrastructures: the role of non-urbanised areas for eco-sustainable planning in a metropolitan region

Non-Urbanised Areas (NUAs) are part of agricultural and green infrastructures that provide ecosystem services. Their role is fundamental for the minimization of urban pollution and adaptation to climate change. Like all natural ecosystems, NUAs are endangered by urban sprawl. The regulation of sprawl is a key issue for land-use planning. We propose a land use suitability strategy model to orient Land Uses of NUAs, based on integration of Land Cover Analysis (LCA) and Fragmentation Analysis (FA). With LCA the percentage of evapotranspiring surface is defined for each land use. Dimensions and densities of NUAs patches are assessed in FA. The model has been developed with Geographical Information Systems, using an extensive set of geodatabases, including orthophotos, vectorial cartographies and field surveys. The case of the municipality of Mascalucia in Catania metropolitan area (Italy), characterized by a considerable urban sprawl, is presented.     

Estimates of AOT ozone indices from time-integrated ozone data and hourly air temperature measurements in southwest Sweden

Surface ozone concentration and surface air temperature was measured hourly at three coastal sites, four low elevation inland sites and two high elevation inland sites in southwestern Sweden. Diurnal ozone concentration range (DOR) and diurnal temperature range (DTR) were strongly correlated, both spatially and temporally, most likely because both depended on atmospheric stability. Accumulated ozone exposure above a threshold concentration of x nmol mol⁻¹ (AOTx) was estimated from time-integrated ozone concentration (as from diffusive sampling) and measures of ozone concentration variability. Two methods both estimated 24-h AOTx with high accuracy (modelling efficiencies >90% for x ≤ 40 nmol mol⁻¹). Daytime (08:00–20:00) AOTx could not be equally well estimated. Estimates were better for lower AOT thresholds. Diffusive ozone concentration sampling, combined with hourly temperature monitoring, could be a valuable complement to ozone concentration monitoring with continuous instruments.