An air quality modeling study comparing two possible sites for the new international airport for Mexico City

Using an air quality model, two future urban scenarios induced by the construction of the new international airport for Mexico City are compared at a regional level. The air quality model couples the meteorology model MM5 and state-of-the-art photochemistry. The air quality comparison is made using metrics for the criterion gases selected for the study. From the two urban scenarios compared, the option for Tizayuca is moderately better than the option for Texcoco, because relative reductions in O3 and other photochemical pollutants are achieved over highly populated areas. Regardless of the site, the air quality for the central region of Mexico in the future will deteriorate. In the region of central Mexico, SO2 and NO2 will become important pollutants.     

Air Quality Impacts of Distributed Energy Resources Implemented in the Northeastern United States

Abstract

Emissions from the potential installation of distributed energy resources (DER) in the place of current utility-scale power generators have been introduced into an emissions inventory of the northeastern United States. A methodology for predicting future market penetration of DER that considers economics and emission factors was used to estimate the most likely implementation of DER. The methodology results in spatially and temporally resolved emission profiles of criteria pollutants that are subsequently introduced into a detailed atmospheric chemistry and transport model of the region. The DER technology determined by the methodology includes 62% reciprocating engines, 34% gas turbines, and 4% fuel cells and other emerging technologies. The introduction of DER leads to retirement of 2625 MW of existing power plants for which emissions are removed from the inventory. The air quality model predicts maximum differences in air pollutant concentrations that are located downwind from the central power plants that were removed from the domain. Maximum decreases in hourly peak ozone concentrations due to DER use are 10 ppb and are located over the state of New Jersey. Maximum decreases in 24-hr average fine particulate matter (PM_2.5) concentrations reach 3 μg/m3 and are located off the coast of New Jersey and New York. The main contribution to decreased PM_2.5 is the reduction of sulfate levels due to significant reductions in direct emissions of sulfur oxides (SO_x) from the DER compared with the central power plants removed. The scenario presented here represents an accelerated DER penetration case with aggressive emission reductions due to removal of highly emitting power plants. Such scenario provides an upper bound for air quality benefits of DER implementation scenarios.     

Temporal and spatial statistical analysis of ambient air quality of Assam (India)

ABSTRACT

Present paper represents the spatio-temporal variation of air quality and performances of geostatistical tools for the identification of pollutants zone in various districts of Assam (India). Geographic Information System (GIS) and geostatistical analysis were utilized to estimate the spatio-temporal variations (2015–2017) of gaseous and particulate air pollutants. Data of 23 fixed monitoring stations were collected from the Central Pollution Control Board (CPCB). It was observed that SO₂ and NO_x concentrations are the major pollutants to the deterioration of air quality in Assam State. Exploratory data analysis was considered for the determination of spatial and temporal patterns of air pollutants. Air Quality index (AQI) was calculated based on the air pollutants and particulate matter. Radial Basis Function (RBF) interpolation techniques were used to analyze the spatial and temporal variation of air quality in Assam. Cross-validation is applied to evaluate the accuracy of interpolation methods in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash–Sutcliffe Equation (NSE) and Accuracy Factor (ACFT). In 2015, the high value of AQI portrayed in the central and northeast of the state. In 2016, the central and entire east of the study area was recorded the highest value of AQI. In 2017, it was observed that mostly the central part of the state recorded the high value of AQI. The spatio-temporal variation trend of air pollutants provides sound scientific basis for its management and control. This information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes.     

Resolving the interactions between population density and air pollution emissions controls in the San Joaquin Valley,USA

The effectiveness of emissions control programs designed to reduce concentrations of airborne particulate matter with an aerodynamic diameter <2.5 μm (PM_2.5) in California's San Joaquin Valley was studied in the year 2030 under three growth scenarios: low, medium, and high population density. Base-case inventories for each choice of population density were created using a coupled emissions modeling system that simultaneously considered interactions between land use and transportation, area source, and point source emissions. The ambient PM_2.5 response to each combination of population density and emissions control was evaluated using a regional chemical transport model over a 3-week winter stagnation episode. Comparisons between scenarios were based on regional average and population-weighted PM_2.5 concentrations. In the absence of any emissions control program, population-weighted concentrations of PM_2.5 in the future San Joaquin Valley are lowest under growth scenarios that emphasize low population density. A complete ban on wood burning and a 90% reduction in emissions from food cooking operations and diesel engines must occur before medium- to high-density growth scenarios result in lower population-weighted concentrations of PM_2.5. These trends partly reflect the fact that existing downtown urban cores that naturally act as anchor points for new high-density growth in the San Joaquin Valley are located close to major transportation corridors for goods movement. Adding growth buffers around transportation corridors had little impact in the current analysis, since the 8-km resolution of the chemical transport model already provided an artificial buffer around major emissions sources.

Assuming that future emissions controls will greatly reduce or eliminate emissions from residential wood burning, food cooking, and diesel engines, the 2030 growth scenario using “as-planned” (medium) population density achieves the lowest population-weighted average PM_2.5 concentration in the future San Joaquin Valley during a severe winter stagnation event.

Implications: The San Joaquin Valley is one of the most heavily polluted air basins in the United States that are projected to experience strong population growth in the coming decades. The best plan to improve air quality in the region combines medium- or high-density population growth with rigorous emissions controls. In the absences of controls, high-density growth leads to increased population exposure to PM_2.5 compared with low-density growth scenarios (urban sprawl).     

An Air Pollution Modeling Study Using Three Surface Coverings Near the New International Airport of Mexico City

Abstract

The dry lakebed of what once was the lake of Texcoco is the location selected for the New International Airport of Mexico City. This project will generate an important urban development near the airport with regional implications on air quality. Using a prognostic air quality model, the consequences of photochemical air pollution in the metropolitan area of Mexico City resulting from three possible coverings for the areas of the lakebed that are not occupied by the runway and terminal building are investigated. These coverings are desert, grassland, and water and occupy an area of 63 km². This study is based on a representative high pollution episode. In addition to reducing the emission of primary natural particles, the water covering generates a land-water breeze capable of maintaining enough ventilation to reduce pollutant concentrations over a localized region of the metropolitan area and may enhance the wind speed on the coasts of the proposed lake.     

Needed: A National R&D Effort To Develop Individual Air Pollution Monitor Instrumentation

The results from the regional air quality analysis for the Four Corners Study are discussed in this paper. This study was one of five regional studies conducted for the National Commission on Air Quality. Potential regional air quality impacts were evaluated through the year 1995 for alternative energy scenarios under current and alternative regulatory policies. The alternative regulatory policies include emission fees, technology standards, emission ceilings, and prevention of significant deterioration class elimination. The alternatives were compared in terms of their impacts on regional visibility and on the ambient concentrations of SO₂, SO₄, and primary fine particulates. The fate of the pollutants was estimated. The alternative regulatory policies were quite different with respect to their control of SO_X emissions and their impacts on regional visibility. Sources located outside of the study region were estimated to have a major impact on regional air quality within the study region.     

Development of a Speciated,Hourly, and Gridded Air Pollutants Emission Modeling System—A Case Study on the Precursors of Photochemical Smog in the Seoul Metropolitan Area,Korea

ABSTRACT

A speciated, hourly, and gridded air pollutants emission modeling system (SHEMS) was developed and applied in predicting hourly nitrogen dioxide (NO₂) and ozone (O₃) levels in the Seoul Metropolitan Area (SMA). The primary goal of the SHEMS was to produce a systemized emission inventory for air pollutants including ozone precursors for modeling air quality in urban areas.

The SHEMS is principally composed of three parts: (1) a pre-processor to process emission factors, activity levels, and spatial and temporal information using a geographical information system; (2) an emission model for each source type; and (3) a post-processor to produce report and input data for air quality models through database modeling. The source categories in SHEMS are point, area, mobile, natural, and other sources such as fugitive emissions. The emission database produced by SHEMS contains 22 inventoried compounds: sulfur dioxide, NO₂, carbon monoxide, and 19 speciated volatile organic compounds. To validate SHEMS, the emission data were tested with the Urban Airshed Model to predict NO₂ and O₃ concentrations in the SMA during selected episode days in 1994. The results turned out to be reliable in describing temporal variation and spatial distribution of those pollutants.     

Simulating Urban-Scale Air Pollutants and Their Predicting Capabilities over the Seoul Metropolitan Area

Abstract

Urban-scale air pollutants for sulfur dioxide, nitrogen dioxide, particulate matter with aerodynamic diameter >10 μm, and ozone (O₃) were simulated over the Seoul metropolitan area, Korea, during the period of July 2-11, 2002, and their predicting capabilities were discussed. The Air Pollution Model (TAPM) and the highly disaggregated anthropogenic and the biogenic gridded emissions (1 km × 1 km) recently prepared by the Korean Ministry of Environment were applied. Wind fields with observational nudging in the prognostic meteorological model TAPM are optionally adopted to comparatively examine the meteorological impact on the prediction capabilities of urban-scale air pollutants. The result shows that the simulated concentrations of secondary air pollutant largely agree with observed levels with an index of agreement (IOA) of >0.6, whereas IOAs of ～0.4 are found for most primary pollutants in the major cities, reflecting the quality of emission data in the urban area. The observationally nudged wind fields with higher IOAs have little effect on the prediction for both primary and secondary air pollutants, implying that the detailed wind field does not consistently improve the urban air pollution model performance if emissions are not well specified. However, the robust highest concentrations are better described toward observations by imposing observational nudging, suggesting the importance of wind fields for the predictions of extreme concentrations such as robust highest concentrations, maximum levels, and >90th percentiles of concentrations for both primary and secondary urban-scale air pollutants.     

Evaluation of Human Exposure to Ambient PM10 in the Metropolitan Area of Mexico City Using a GIS-Based Methodology

ABSTRACT

The main goal of this study was to evaluate the magnitude of outdoor exposure to fine particulate matter (PM₁₀) potentially experienced by the population of metropolitan Mexico City. With the use of a geographic information system (GIS), spatially resolved PM₁₀ distributions were generated and linked to the local population. The PM₁₀ concentration exceeded the 24-hr air quality standard of 150 μg/m³ on 16% of the days, and the annual air quality standard of 50 μg/m³ was exceeded by almost twice its value in some places. The basic methodology described in this paper integrates spatial demographic and air quality databases, allowing the evaluation of various air pollution reduction scenarios. Achieving the annual air quality standard would represent a reduction in the annual arithmetic average concentration of 14 μg/m³ for the typical inhabitant. Human exposure to particulate matter (PM) has been associated with mortality and morbidity in Mexico City; reducing the concentration levels of this pollutant would represent a reduction in mortality and morbidity and the associated cost of such impacts. This methodology is critical to assessing the potential benefits of the current initiative to improve air quality implemented by the Environmental Metropolitan Commission of Mexico City.     

Daily Mortality and Air Pollution in the Netherlands

ABSTRACT

We studied the association of daily mortality with short-term variations in the ambient concentrations of major gaseous pollutants and PM in the Netherlands. The magnitude of the association in the four major urban areas was compared with that in the remainder of the country. Daily cause-specific mortality counts, air quality, temperature, relative humidity, and influenza data were obtained from 1986 to 1994. The relationship between daily mortality and air pollution was modeled using Poisson regression analysis. We adjusted for potential confounding due to long-term and seasonal trends, influenza epidemics, ambient temperature and relative humidity, day of the week, and holidays, using generalized additive models.

Influenza episodes were associated with increased mortality up to 3 weeks later. Daily mortality was significantly associated with the concentration of all air pollutants. An increase in the PM₁₀ concentration by 100 u.g/m³ was associated with a relative risk (RR) of 1.02 for total mortality. The largest RRs were found for pneumonia deaths. Ozone had the most consistent, independent association with mortality. Particulate air pollution (e.g., PM₁₀, black smoke [BS]) was not more consistently associated with mortality than were the gaseous pollutants SO₂ and NO₂. Aerosol SO₄ ^-2, NO₃ ^-, and BS were more consistently associated with total mortality than was PM₁₀. The RRs for all pollutants were substantially larger in the summer months than in the winter months. The RR of total mortality for PM₁₀ was 1.10 for the summer and 1.03 for the winter. There was no consistent difference between RRs in the four major urban areas and the more rural areas.     

Derivation of exemption formulas for air quality regulatory applications

The regulatory agencies and the industries have the responsibility for assessing the environmental impact from the release of air pollutants, and for protecting environment and public health. The simple exemption formula is often used as a criterion for the purpose of screening air pollutants. That is, the exemption formula is used for air quality review and to determine whether a facility applying for and described in a new, modified, or revised air quality plan is exempted from further air quality review. The Bureau of Ocean Energy Management’s (BOEM) air quality regulations are used to regulate air emissions and air pollutants released from the oil and gas facilities in the Gulf of Mexico. If a facility is not exempt after completing the air quality review, a refined air quality modeling will be required to regulate the air pollutants. However, at present, the scientific basis for BOEM’s exemption formula is not available to the author. Therefore, the purpose of this paper is to provide the theoretical framework and justification for the use of BOEM’s exemption formula. In this paper, several exemption formulas have been derived from the Gaussian and non-Gaussian dispersion models; the Gaussian dispersion model is a special case of non-Gaussian dispersion model. The dispersion parameters obtained from the tracer experiments in the Gulf of Mexico are used in the dispersion models. In this paper, the dispersion parameters used in the dispersion models are also derived from the Monin-Obukhov similarity theory. In particular, it has been shown that the total amount of emissions from the facility for each air pollutant calculated using BOEM’s exemption formula is conservative.

Implications:?The operation of offshore oil and gas facilities under BOEM’s jurisdiction is required to comply with the BOEM’s regulations. BOEM’s air quality regulations are used to regulate air emissions and air pollutants released from the oil and gas facilities in the Gulf of Mexico. The exemption formulas have been used by BOEM and other regulatory agencies as a screening tool to regulate air emissions emitted from the oil and gas and other industries. Because of the BOEM’s regulatory responsibility, it is important to establish the scientific basis and provide the justification for the exemption formulas. The methodology developed here could also be adopted and used by other regulatory agencies.     

Air pollution in cities

Air quality in cities is the result of a complex interaction between natural and anthropogenic environmental conditions. Air pollution in cities is a serious environmental problem – especially in the developing countries. The air pollution path of the urban atmosphere consists of emission and transmission of air pollutants resulting in the ambient air pollution. Each part of the path is influenced by different factors. Emissions from motor traffic are a very important source group throughout the world. During transmission, air pollutants are dispersed, diluted and subjected to photochemical reactions. Ambient air pollution shows temporal and spatial variability. As an example of the temporal variability of urban air pollutants caused by motor traffic, typical average annual, weekly and diurnal cycles of NO, NO₂, O₃ and O_x are presented for an official urban air-quality station in Stuttgart, southern Germany. They are supplemented by weekly and diurnal cycles of selected percentile values of NO, NO₂, and O₃. Time series of these air pollutants give information on their trends. Results are discussed with regard to air pollution conditions in other cities. Possibilities for the assessment of air pollution in cities are shown. In addition, a qualitative overview of the air quality of the world's megacities is given.     

Air quality modeling for effective environmental management in the mining region

Air quality in the mining sector is a serious environmental concern and associated with many health issues. Air quality management in mining regions has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanisms. A modeling approach called the mining air dispersion model (MADM) is developed to predict air pollutants concentration in the mining region while considering the deposition effect. The model takes into account the planet’s boundary conditions and assumes that the eddy diffusivity depends on the downwind distance. The developed MADM is applied to a mining site in Canada. The model provides values for the predicted concentrations of PM₁₀, PM_2.5, TSP, NO₂, and six heavy metals (As, Pb, Hg, Cd, Zn, Cr) at various receptor locations. The model shows that neutral stability conditions are dominant for the study site. The maximum mixing height is achieved (1280 m) during the evening in summer, and the minimum mixing height (380 m) is attained during the evening in winter. The dust fall (PM coarse) deposition flux is maximum during February and March with a deposition velocity of 4.67 cm/sec. The results are evaluated with the monitoring field values, revealing a good agreement for the target air pollutants with R-squared ranging from 0.72 to 0.96 for PM_2.5, from 0.71 to 0.82 for PM₁₀, and from 0.71 to 0.89 for NO₂. The analyses illustrate that the presented algorithm in this model can be used to assess air quality for the mining site in a systematic way. Comparisons of MADM and CALPUFF modeling values are made for four different pollutants (PM_2.5, PM₁₀, TSP, and NO₂) under three different atmospheric stability classes (stable, neutral, and unstable). Further, MADM results are statistically tested against CALPUFF for the air pollutants and model performance is found satisfactory.

Implications: The mathematical model (MADM) is developed by extending the Gaussian equation particularly when examining the settling process of important pollutants for the industrial region. Physical removal effects of air pollutants with field data have been considerred for the MADM development and for an extensive field case study. The model is well validated in the field of an open pit mine to assess the regional air quality. The MADA model helps to facilitate the management of the mining industry in doing estimation of emission rate around mining activities and predicting the resulted concentration of air pollutants together in one integrated approach.     

Analysis of Air Quality Observations with the Aid of the Source-Receptor Relationship Approach

Abstract

In this study, an attempt was made to analyze time series of air quality measurements (O₃, SO₂, SO₄ ^2?NO_x) conducted at a remote place in the eastern Mediterranean (Finokalia at Crete Island in 1999) to obtain concrete information on potential contributions from emission sources. For the definition of a source-receptor relationship, advanced meteorological and dispersion models appropriate to identify “areas of influence” have been used. The model tools used are the Regional Atmospheric Modeling System and the Lagrangian-type particle dispersion model (forward and backward in time), with capabilities to derive influence functions and definition of “areas of influence.” When high levels of pollutants have been measured at the remote location of Finokalia, particles are released from this location (receptor) and traced backward in time. The influence function derived from particle distributions characterizes dispersion conditions in the atmosphere and also provides information on potential contributions from emission sources within the modeling domain to this high concentration. As was shown in the simulation results, the experimental site of Finokalia in Crete is influenced during the selected case studies, primarily by pollutants emitted from the urban conglomerate of Athens. Secondarily, it is influenced by polluted air masses arriving from Italy and/or the Black Sea Region. For some specific cases, air pollutants monitored at Finokalia were possibly related to war activities in the West Balkan Region (Kosovo).     

Assessment of the emissions and air quality impacts of biomass and biogas use in California

It is estimated that there is sufficient in-state “technically” recoverable biomass to support nearly 4000 MW of bioelectricity generation capacity. This study assesses the emissions of greenhouse gases and air pollutants and resulting air quality impacts of new and existing bioenergy capacity throughout the state of California, focusing on feedstocks and advanced technologies utilizing biomass resources predominant in each region. The options for bioresources include the production of bioelectricity and renewable natural gas (NG). Emissions of criteria pollutants and greenhouse gases are quantified for a set of scenarios that span the emission factors for power generation and the use of renewable natural gas for vehicle fueling. Emissions are input to the Community Multiscale Air Quality (CMAQ) model to predict regional and statewide temporal air quality impacts from the biopower scenarios. With current technology and at the emission levels of current installations, maximum bioelectricity production could increase nitrogen oxide (NO_x) emissions by 10% in 2020, which would cause increases in ozone and particulate matter concentrations in large areas of California. Technology upgrades would achieve the lowest criteria pollutant emissions. Conversion of biomass to compressed NG (CNG) for vehicles would achieve comparable emission reductions of criteria pollutants and minimize emissions of greenhouse gases (GHG). Air quality modeling of biomass scenarios suggest that applying technological changes and emission controls would minimize the air quality impacts of bioelectricity generation. And a shift from bioelectricity production to CNG production for vehicles would reduce air quality impacts further. From a co-benefits standpoint, CNG production for vehicles appears to provide the best benefits in terms of GHG emissions and air quality.

Implications:?This investigation provides a consistent analysis of air quality impacts and greenhouse gas emissions for scenarios examining increased biomass use. Further work involving economic assessment, seasonal or annual emissions and air quality modeling, and potential exposure analysis would help inform policy makers and industry with respect to further development and direction of biomass policy and bioenergy technology alternatives needed to meet energy and environmental goals in California.     

Toward verifying fossil fuel CO2 emissions with the CMAQ model: Motivation,model description and initial simulation

Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO₂ spatiotemporal variability and verify CO₂ fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO₂. This paper presents methods, input data, and initial results for CO₂ simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere–atmosphere exchange, and meteorology in regulating the spatial distribution of CO₂ near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO₂ concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver, CO, the CMAQ model using hourly varying, high-resolution CO₂ fossil-fuel emissions from the Vulcan inventory and CarbonTracker optimized NEE reproduced the observed diurnal profile of CO₂ reasonably well but with a low bias in the early morning. The spatial distribution of CO₂ was found to correlate with NOx, SO₂, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO₂ observations and understand CO₂ variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO₂ source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO₂ spatiotemporal variability and various uncertainties in the future.

Implications: Atmospheric CO₂ has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO₂ variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO₂ emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO₂ as an air pollutant.     

Estimating changes in urban ozone concentrations due to life cycle emissions from hydrogen transportation systems

Hydrogen has been proposed as a low polluting alternative transportation fuel that could help improve urban air quality. This paper examines the potential impact of introducing a hydrogen-based transportation system on urban ambient ozone concentrations. This paper considers two scenarios, where significant numbers of new hydrogen vehicles are added to a constant number of gasoline vehicles. In our scenarios hydrogen fuel cell vehicles (HFCVs) are introduced in Sacramento, California at market penetrations of 9% and 20%. From a life cycle analysis (LCA) perspective, considering all the emissions involved in producing, transporting, and using hydrogen, this research compares three hypothetical natural gas to hydrogen pathways: (1) on-site hydrogen production; (2) central hydrogen production with pipeline delivery; and (3) central hydrogen production with liquid hydrogen truck delivery. Using a regression model, this research shows that the daily maximum temperature correlates well with atmospheric ozone formation. However, increases in initial VOC and NO_x concentrations do not necessarily increase the peak ozone concentration, and may even cause it to decrease. It is found that ozone formation is generally limited by NO_x in the summer and is mostly limited by VOC in the fall in Sacramento. Of the three hydrogen pathways, the truck delivery pathway contributes the most to ozone precursor emissions. Ozone precursor emissions from the truck pathway at 9% market penetration can cause additional 3-h average VOC (or NO_x) concentrations up to approximately 0.05% (or 1%) of current pollution levels, and at 20% market penetration up to approximately 0.1% (or 2%) of current pollution levels. However, all of the hydrogen pathways would result in very small (either negative or positive) changes in ozone air quality. In some cases they will result in worse ozone air quality (mostly in July, August, and September), and in some cases they will result in better ozone air quality (mostly in October). The truck pathway tends to cause a much wider fluctuation in degradation or improvement of ozone air quality: percentage changes in peak ozone concentrations are approximately −0.01% to 0.04% for the assumed 9% market penetration, and approximately −0.03% to 0.1% for the 20% market penetration. Moreover, the 20% on-site pathway occasionally results in a decrease of about −0.1% of baseline ozone pollution. Compared to the current ambient pollution level, all three hydrogen pathways are unlikely to cause a serious ozone problem for market penetration levels of HFCVs in the 9–20% range.     

Improving Urban Air Quality in China: Beijing Case Study

Abstract

China is undergoing rapid urbanization because of unprecedented economic growth. As a result, many cities suffer from air pollution. Two-thirds of China’s cities have not attained the ambient air quality standards applicable to urban residential areas (Grade II). Particulate matter (PM), rather than sulfur dioxide (SO₂), is the major pollutant reflecting the shift from coal burning to mixed source pollution. In 2002, 63.2 and 22.4% of the monitored cities have PM and SO₂ concentrations exceeding the Grade II standard, respectively. Nitrogen oxides (NO_x) concentration kept a relatively stable level near the Grade II standard in the last decade and had an increasing potential in recent years because of the rapid motorization. In general, the air pollutants emission did not increase as quickly as the economic growth and energy consumption, and air quality in Chinese cities has improved to some extent. Beijing, a typical representative of rapidly developing cities, is an example to illustrate the possible options for urban air pollution control. Beijing’s case provides hope that the challenges associated with improving air quality can be met during a period of explosive development and motorization.     

Assessing the spatial distribution of nitrogen dioxide in London,Ontario

Land use regression (LUR) models have been widely used to characterize the spatial distribution of urban air pollution and estimate exposure in epidemiologic studies. However, spatial patterns of air pollution vary greatly between cities due to local source type and distribution. London, Ontario, Canada, is a medium-sized city with relatively few and isolated industrial point sources, which allowed the study to focus on the contribution of different transportation sectors to urban air pollution. This study used LUR models to estimate the spatial distribution of nitrogen dioxide (NO₂) and to identify local sources influencing NO₂ concentrations in London, ON. Passive air sampling was conducted at 50 locations throughout London over a 2-week period in May–June 2010. NO₂ concentrations at the monitored locations ranged from 2.8 to 8.9 ppb, with a median of 5.2 ppb. Industrial land use, dwelling density, distance to highway, traffic density, and length of railways were significant predictors of NO₂ concentrations in the final LUR model, which explained 78% of NO₂ variability in London. Traffic and dwelling density explained most of the variation in NO₂ concentrations, which is consistent with LUR models developed in other Canadian cities. We also observed the importance of local characteristics. Specifically, 17% of the variation was explained by distance to highways, which included the impacts of heavily traveled corridors transecting the southern periphery of the city. Two large railway yards and railway lines throughout central areas of the city explained 9% of NO₂ variability. These results confirm the importance of traditional LUR variables and highlight the importance of including a broader array of local sources in LUR modeling. Finally, future analyses will use the model developed in this study to investigate the association between ambient air pollution and cardiovascular disease outcomes, including plaque burden, cholesterol, and hypertension.

Implications: Monitoring and modeling of NO₂ throughout the city of London represents an important step toward assessing air pollution health effects in a mid-sized Canadian city. The study supports the introduction of railways to LUR modeling of NO₂. Railways explained approximately 9% of the variability in ambient NO₂ concentrations in London, which suggests that local sources captured by land-use indicators may contribute to the efficacy of LUR models. These findings provide insights relevant to other medium and smaller sized cities with similar land use and transportation infrastructure. Furthermore, London is a central hub for medical research and treatment in southwestern Ontario, with facilities such as the Robarts Research Institute, London Regional Cancer Program (LRCP), and Stroke Prevention & Atherosclerosis Research Centre (SPARC). The models developed in this study will provide estimates of exposure for future analyses examining air pollution health effects in this data-rich population.     

A gas/aerosol air pollutants study over the urban area of Rome using a comprehensive chemical transport model

The aim of this study was to identify areas of potential relevant exposure to pollutants within Rome's urban core. To meet this goal, intensive field campaigns were conducted and simulations were performed, using the flexible air quality regional model (FARM), to study winter and summer pollution episodes. The simulations were performed using a complete emission inventory that included traffic flow model results of the Roman street network to better describe, with respect to the available diffuse national emission inventory, the hourly variation of traffic emissions in the city. The meteorological reconstruction was performed by means of both prognostic and diagnostic models by using experimental data collected during the field campaigns. To evaluate the capability of the FARM model to capture the main features of the selected episodes, a comparison of modelled results against observed air quality data for different pollutants was performed at urban and rural sites. FARM performed well in predicting ozone (O₃) and nitrogen dioxide (NO₂) concentrations, showing a good reproduction of both daily peaks and their diurnal variations. The model also showed a good capability to reproduce the magnitude of volatile alkane, aromatic and carbonyl compound concentrations. PM₁₀ model results revealed the tendency to under-predict the observed values. PM composition model results were compared with observed data, evidencing good results for elemental carbon (EC), nitrate (NO₃⁻) and ammonium (NH₄⁺), underestimation for sulphate (SO₄²⁻) and poor performance for organic matter (OM). The soil components of PM were found to be significantly under-predicted by the model, especially during Saharan dust episodes. Overall, the study results show large areas of high O₃ and PM₁₀ concentrations where levels of pollutants should be carefully monitored and population exposure evaluated.