Modeling Pareto efficient PM10 control policies in Northern Italy to reduce health effects

High PM10 concentrations can cause human health problems, both related to short-term and long-term exposure to particles. In this work the impact of efficient PM10 control problems in Northern Italy is assessed by means of a two-stage methodology. In the first stage a multi-objective optimization approach is applied. The multi-objective problem defines two control objectives (the emission reduction costs and the air quality index) to be minimized varying the decision variables (precursor emission reductions). The solution of the multi-objective problem is the Pareto efficient PM10 control policies. In the second stage, the ExternE methodology is applied to estimate health impacts and external costs for the efficient emission reduction scenarios computed in the first stage. The methodology has been applied over Lombardia region, one of the most polluted areas in Europe.     

Neuro-fuzzy and neural network systems for air quality control

In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source–receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source–receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source–receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source–receptor models are able to accurately reproduce the simulation of the 3D modelling system.     

Air quality impacts of distributed energy resources implemented in the northeastern United States

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 (PM2.5) concentrations reach 3 microg/m3 and are located off the coast of New Jersey and New York. The main contribution to decreased PM2.5 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.     

Estimating the emission source reduction of PM10 in central Taiwan

Three theoretical parent frequency distributions; lognormal, Weibull and gamma were used to fit the complete set of PM10 data in central Taiwan. The gamma distribution is the best one to represent the performance of high PM10 concentrations. However, the parent distribution sometimes diverges in predicting the high PM10 concentrations. Therefore, two predicting methods, Method I: two-parameter exponential distribution and Method II: asymptotic distribution of extreme value, were used to fit the high PM10 concentration distributions more correctly. The results fitted by the two-parameter exponential distribution are better matched with the actual high PM10 data than that by the parent distributions. Both of the predicting methods can successfully predict the return period and exceedances over a critical concentration in the future year. Moreover, the estimated emission source reductions of PM10 required to meet the air quality standard by Method I and Method II are very close. The estimated emission source reductions of PM10 range from 34% to 48% in central Taiwan.     

Modeling exposure to particulate matter

Exposure assessment, a component of risk assessment, links sources of pollution with health effects. Exposure models are scientific tools used to gain insights into the processes affecting exposure assessment. The purpose of this paper is to review the process and methodology of estimating inhalation exposure to particulate matter (PM) using various types of models. Three types of models are discussed in the paper. Indirect type of models are physical models that employ inventories of outdoor and indoor sources and their emission rates to identify major sources contributing to exposure to PM, and use fate and transport and indoor air quality models to estimate PM concentrations at receptor sites. PM concentrations and time spent by a subject at each receptor site are input variables to the conventional exposure model that estimates the desired exposure levels. Direct type models use measured exposure or exposure concentrations in conjunction with information obtained from questionnaires to formulate exposure regression models. Stochastic models use exposure measurements, estimates can also be used, to formulate exposure population distributions and investigate associated uncertainty and variability. Since models developed using databases from western countries are not necessarily applicable in developing countries, the difference in requirements among western and developing countries is highlighted in the paper. Employment of exposure modeling methods in developing countries requires development of local information. Such information includes local outdoor and indoor source inventories, local or regional meteorological conditions, adjustment of indoor models to reflect local building construction conditions, and use of questionnaires to obtain local time budget and activity patterns of the subject population.     

Effectiveness of emission control technologies for auxiliary engines on ocean-going vessels

Large auxiliary engines operated on ocean-going vessels in transit and at berth impact the air quality of populated areas near ports. This paper presents new information on the comparison of emission ranges from three similar engines and the effectiveness of three control technologies: switching to cleaner burning fuels, operating in the low oxides of nitrogen (NOx) mode, and selective catalytic reduction (SCR). In-use measurements of gaseous (NOx, carbon monoxide [CO], carbon dioxide [CO2]) and fine particulate matter (PM2.5; total and speciated) emissions were made on three auxiliary engines on post-PanaMax class container vessels following the International Organization for Standardization-8178-1 protocol. The in-use NOx emissions for the MAN B&W 7L32/40 engine family vary from 15 to 21.1 g/kW-hr for heavy fuel oil and 8.9 to 19.6 g/kW-hr for marine distillate oil. Use of cleaner burning fuels resulted in NOx reductions ranging from 7 to 41% across different engines and a PM2.5 reduction of up to 83%. The NOx reductions are a consequence of fuel nitrogen content and engine operation; the PM2.5 reduction is attributed to the large reductions in the hydrated sulfate and organic carbon (OC) fractions. As expected, operating in the low-NOx mode reduced NOx emissions by approximately 32% and nearly doubled elemental carbon (EC) emissions. However, PM2.5 emission factors were nearly unchanged because the EC emission factor is only approximately 5% of the total PM2.5 mass. SCR reduced the NOx emission factor to less than 2.4 g/kW-hr, but it increased the PM2.5 emissions by a factor of 1.5-3.8. This increase was a direct consequence of the conversion of sulfur dioxide to sulfate emissions on the SCR catalyst. The EC and OC fractions of PM2.5 reduced across the SCR unit.     

Boiler briquette coal versus raw coal: Part II--Energy, greenhouse gas, and air quality implications

The objective of this paper is to conduct an integrated analysis of the energy, greenhouse gas, and air quality impacts of a new type of boiler briquette coal (BB-coal) in contrast to those of the raw coal from which the BB-coal was formulated (R-coal). The analysis is based on the source emissions data and other relevant data collected in the present study and employs approaches including the construction of carbon, energy, and sulfur balances. The results show that replacing R-coal with BB-coal as the fuel for boilers such as the one tested would have multiple benefits, including a 37% increase in boiler thermal efficiency, a 25% reduction in fuel demand, a 26% reduction in CO2 emission, a 17% reduction in CO emission, a 63% reduction in SO2 emission, a 97% reduction in fly ash and fly ash carbon emission, a 22% reduction in PM2.5 mass emission, and a 30% reduction in total emission of five toxic hazardous air pollutant (HAP) metals contained in PM10. These benefits can be achieved with no changes in boiler hardware and with a relatively small amount of tradeoffs: a 30% increase in PM10 mass emission and a 9-16% increase in fuel cost.     

Codependencies of reactive air toxic and criteria pollutants on emission reductions

It is important to understand the effects of emission controls on concentrations of ozone, fine particulate matter (PM2.5), and hazardous air pollutants (HAPs) simultaneously, to evaluate the full range of health, ecosystem, and economic effects. Until recently, the capability to simultaneously evaluate interrelated atmospheric pollutants ("one atmosphere" analysis) was unavailable to air quality managers. In this work, we use an air quality model to examine the potential effect of three emission reductions on concentrations of ozone, PM2.5, and four important HAPs (formaldehyde, acetaldehyde, acrolein, and benzene) over a domain centered on Philadelphia for 12-day episodes in July and January 2001. Although NO(x) controls are predicted to benefit PM2.5 concentrations and sometimes benefit ozone, they have only a small effect on formaldehyde, slightly increase acetaldehyde and acrolein, and have no effect on benzene in the July episode. Concentrations of all pollutants except benzene increase slightly with NO(x) controls in the January simulation. Volatile organic compound controls alone are found to have a small effect on ozone and PM2.5, a less than linear effect on decreasing aldehydes, and an approximately linear effect on acrolein and benzene in summer, but a slightly larger than linear effect on aldehydes and acrolein in winter. These simulations indicate the difficulty in assessing how toxic air pollutants might respond to emission reductions aimed at decreasing criteria pollutants such as ozone and PM2.5.     

Evaluation of human exposure to ambient PM10 in the metropolitan area of Mexico City using a GIS-based methodology.

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 microg/m3 on 16% of the days, and the annual air quality standard of 50 microg/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 microg/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.     

Identification and chemical characterization of industrial particulate matter sources in southwest Spain

A detailed physical and chemical characterization of coarse particulate matter (PM10) and fine particulate matter (PM2.5) in the city of Huelva (in Southwestern Spain) was carried out during 2001 and 2002. To identify the major emission sources with a significant influence on PM10 and PM2.5, a methodology was developed based on the combination of: (1) real-time measurements of levels of PM10, PM2.5, and very fine particulate matter (PM1); (2) chemical characterization and source apportionment analysis of PM10 and PM2.5; and (3) intensive measurements in field campaigns to characterize the emission plumes of several point sources. Annual means of 37, 19, and 16 microg/m3 were obtained for the study period for PM10, PM2.5, and PM1, respectively. High PM episodes, characterized by a very fine grain size distribution, are frequently detected in Huelva mainly in the winter as the result of the impact of the industrial emission plumes on the city. Chemical analysis showed that PM at Huelva is characterized by high PO4(3-) and As levels, as expected from the industrial activities. Source apportionment analyses identified a crustal source (36% of PM10 and 31% of PM2.5); a traffic-related source (33% of PM10 and 29% of PM2.5), and a marine aerosol contribution (only in PM10, 4%). In addition, two industrial emission sources were identified in PM10 and PM2.5: (1) a petrochemical source, 13% in PM10 and 8% in PM2.5; and (2) a mixed metallurgical-phosphate source, which accounts for 11-12% of PM10 and PM2.5. In PM2.5 a secondary source has been also identified, which contributed to 17% of the mass. A complete characterization of industrial emission plumes during their impact on the ground allowed for the identification of tracer species for specific point sources, such as petrochemical, metallurgic, and fertilizer and phosphate production industries.     

Impact of biogenic emission uncertainties on the simulated response of ozone and fine particulate matter to anthropogenic emission reductions

The role of emissions of volatile organic compounds and nitric oxide from biogenic sources is becoming increasingly important in regulatory air quality modeling as levels of anthropogenic emissions continue to decrease and stricter health-based air quality standards are being adopted. However, considerable uncertainties still exist in the current estimation methodologies for biogenic emissions. The impact of these uncertainties on ozone and fine particulate matter (PM2.5) levels for the eastern United States was studied, focusing on biogenic emissions estimates from two commonly used biogenic emission models, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Biogenic Emissions Inventory System (BEIS). Photochemical grid modeling simulations were performed for two scenarios: one reflecting present day conditions and the other reflecting a hypothetical future year with reductions in emissions of anthropogenic oxides of nitrogen (NOx). For ozone, the use of MEGAN emissions resulted in a higher ozone response to hypothetical anthropogenic NOx emission reductions compared with BEIS. Applying the current U.S. Environmental Protection Agency guidance on regulatory air quality modeling in conjunction with typical maximum ozone concentrations, the differences in estimated future year ozone design values (DVF) stemming from differences in biogenic emissions estimates were on the order of 4 parts per billion (ppb), corresponding to approximately 5% of the daily maximum 8-hr ozone National Ambient Air Quality Standard (NAAQS) of 75 ppb. For PM2.5, the differences were 0.1-0.25 microg/m3 in the summer total organic mass component of DVFs, corresponding to approximately 1-2% of the value of the annual PM2.5 NAAQS of 15 microg/m3. Spatial variations in the ozone and PM2.5 differences also reveal that the impacts of different biogenic emission estimates on ozone and PM2.5 levels are dependent on ambient levels of anthropogenic emissions.     

Managing emissions from highly industrialized areas: Regulatory compliance under uncertainty

The use of regulatory and compliance-based modeling for air quality impact assessment is invariably relied upon to predict future air quality under various management scenarios particularly where air quality monitoring data are limited. This paper examines the dispersion from a multi-stack cement manufacturing complex with associated quarries and transport activities for regulatory compliance under uncertain emission and meteorological conditions. The concentrations of CO, NO_x, SO₂ and PM at sensitive receptor locations were used as indicators in comparison to World Health Organization (WHO) interim guidelines. Exceedance exposure areas were delineated under bounded uncertainties in input emission factors and meteorological parameters. Planning and management initiatives were tested to control/minimize potential exposure. Compared to the case of low emissions and actual meteorological conditions, the consideration of worst emissions coupled to worst meteorological conditions enlarged the boundaries of the exceedance exposure areas considerably. The implementation of best available technologies and enforcement of emission standards improved air quality in the region significantly and lowered the exposure at many population centers to below health standards. Uncertainty in the output of atmospheric dispersion models continues to play a significant role to be considered at the point where science is translated into political decision making.     

Directions for Combustion Engine Aerosol Measurement in the 21st Century

Abstract

The Coordinating Research Council convened two Real-Time PM Measurement Workshops in December 2008 and March 2009 to take an intensive look at the current status and future directions of combustion aerosol measurement. The purpose was to examine the implications of parallel rapid developments over the past decade in ambient aerosol science, engine aftertreatment technology, and aerosol measurement methodology, which provide bene?ts and challenges to the stakeholders in air quality management. The workshops were organized into sessions targeting key issues in ambient and source combustion particulate matter (PM). These include (1) metrics to characterize and quantify PM, (2) the need to reconcile ambient and source measurements, (3) the role of atmospheric transformations on modeling emissions and exposures, (4) the impact of sampling conditions on PM measurement, and (5) the potential bene?ts of novel PM instrumentation. This paper distills the material presented by subject experts and the insights derived from the in-depth discussions that formed the core of each session. The paper’s objectives are to identify areas of consensus that allow wider practical application of the past decade’s advances in combustion aerosol measurement to improve emissions and air quality modeling, develop emissions reduction strategies, and to recommend directions for progress on issues in which uncertainties remain.     

Development of a microscale emission factor model for particulate matter for predicting real-time motor vehicle emissions

The U.S. Environmental Protection Agency's National Exposure Research Laboratory is pursuing a project to improve the methodology for modeling human exposure to motor vehicle emissions. The overall project goal is to develop improved methods for modeling the source through the air pathway to human exposure in significant exposure microenvironments. Current particulate matter (PM) emission models, particle emission factor model (used in the United States, except California) and motor vehicle emission factor model (used in California only), are suitable only for county-scale modeling and emission inventories. There is a need to develop a site-specific real-time emission factor model for PM emissions to support human exposure studies near roadways. A microscale emission factor model for predicting site-specific real-time motor vehicle PM (MicroFacPM) emissions for total suspended PM, PM less than 10 microm aerodynamic diameter, and PM less than 2.5 microm aerodynamic diameter has been developed. The algorithm used to calculate emission factors in MicroFacPM is disaggregated, and emission factors are calculated from a real-time fleet, rather than from a fleet-wide average estimated by a vehicle-miles-traveled weighting of the emission factors for different vehicle classes. MicroFacPM requires input information necessary to characterize the site-specific real-time fleet being modeled. Other variables required include average vehicle speed, time and day of the year, ambient temperature, and relative humidity.     

Evaluating inspection and maintenance programs: a policy-making framework

This article presents a new analysis approach to design and evaluate motor vehicle inspection and maintenance (I/M) programs. The new approach, called I/M-Design, uses real-world data to provide two resources not previously available: (1) a transparent framework to quantitatively illustrate the range of emission reductions available from I/M, and (2) a sensitivity analysis tool to evaluate how key variables affect I/M performance. In addition, the approach satisfies a policy-making information need--how to convey, in a logical and straightforward manner, the expected benefits from I/M without relying on modeling tools inaccessible to those outside the air quality field. The material presented in this article illustrates the new approach by estimating hydrocarbon (HC) emission reduction benefits available from enhanced I/M in southern California's South Coast Air Basin. I/M-Design estimates that enhanced I/M results in a 14-28% reduction in light-duty motor vehicle HC exhaust emissions; this estimate compares well to other California I/M program evaluations. Even more importantly, I/M-Design sensitivity analyses illustrate how I/M programs that implement stringent failure thresholds, motivate pre-test repair work, and improve repair effectiveness can provide emission reductions that substantially exceed the performance of existing programs.     

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.     

CityDelta: A model intercomparison study to explore the impact of emission reductions in European cities in 2010

This paper gives an overview of the set up, methodology and the obtained results of the CityDelta (phase 1 and 2) project. In the context of the Clean Air For Europe programme of the European Commission, the CityDelta project was designed to evaluate the impact of emission-reduction strategies on air quality at the European continental scale and in European cities. Ozone and particulate matter (PM) are the main components that have been studied. To achieve this goal, a model intercomparison study was organized with the participation of more than 20 modelling groups with a large number of modelling configurations. Two following main topics can be identified in the project. First, in order to evaluate their strengths and weaknesses, the participating models were evaluated against observations in a control year (1999). An accompanying paper will discuss in detail this evaluation aspect for four European cities. The second topic is the actual evaluation of the impact of emission reductions on levels of ozone and PM, with particular attention to the differences between large-scale and fine-scale models. An accompanying paper will discuss this point in detail. In this overview paper the main input to the intercomparison is described as well as the use of the ensemble approach. Finally, attention is given to the policy relevant issue on how to implement the urban air quality signal into large-scale air quality models through the use of functional relationships.     

Forecasting Daily Source Air Quality Using Multivariate Statistical Analysis and Radial Basis Function Networks

Abstract

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.     

A modeling study of coarse particulate matter pollution in Beijing: regional source contributions and control implications for the 2008 Summer Olympics

In the last 10 yr, Beijing has made a great effort to improve its air quality. However, it is still suffering from regional coarse particulate matter (PM10) pollution that could be a challenge to the promise of clean air during the 2008 Olympics. To provide scientific guidance on regional air pollution control, the Mesoscale Modeling System Generation 5 (MM5) and the Models-3/Community Multiscale Air Quality Model (CMAQ) air quality modeling system was used to investigate the contributions of emission sources outside the Beijing area to pollution levels in Beijing. The contributions to the PM10 concentrations in Beijing were assessed for the following sources: power plants, industry, domestic sources, transportation, agriculture, and biomass open burning. In January, it is estimated that on average 22% of the PM10 concentrations can be attributed to outside sources, of which domestic and industrial sources contributed 37 and 31%, respectively. In August, as much as 40% of the PM10 concentrations came from regional sources, of which approximately 41% came from industry and 31% from power plants. However, the synchronous analysis of the hourly concentrations, regional contributions, and wind vectors indicates that in the heaviest pollution periods the local emission sources play a more important role. The implications are that long-term control strategies should be based on regional-scale collaborations, and that emission abatement of local sources may be more effective in lowering the PM10 concentration levels on the heavy pollution days. Better air quality can be attained during the Olympics by placing effective emission controls on the local sources in Beijing and by controlling emissions from industry and power plants in the surrounding regions.     

Natural emissions for regional modeling of background ozone and particulate matter and impacts on emissions control strategies

Natural emissions adopted in current regional air quality modeling are updated to better describe natural background ozone and PM concentrations for North America. The revised natural emissions include organosulfur from the ocean, NO from lightning, sea salt, biogenic secondary organic aerosol (SOA) precursors, and pre-industrial levels of background methane. The model algorithm for SOA formation was also revised. Natural background ozone concentrations increase by up to 4 ppb in annual average over the southeastern US and Gulf of Mexico due to added NO from lightning while the revised biogenic emissions produced less ozone in the central and western US. Natural PM_2.5 concentrations generally increased with the revised natural emissions. Future year (2018) simulations were conducted for several anthropogenic emission reduction scenarios to assess the impact of the revised natural emissions on anthropogenic emission control strategies. Overall, the revised natural emissions did not significantly alter the ozone responses to the emissions reductions in 2018. With revised natural emissions, ozone concentrations were slightly less sensitive to reducing NOx in the southeastern US than with the current natural emissions due to higher NO from lightning. The revised natural emissions have little impact on modeled PM_2.5 responses to anthropogenic emission reductions. However, there are substantial uncertainties in current representations of natural sources in air quality models and we recommend that further study is needed to refine these representations.