Current and future linked responses of ozone and PM2.5 to emission controls

Synergistic PM2.5 and O3 control to address the emerging global PM2.5-O3 compound pollution challenges

In recent years, the issue of PM2.5-O3 compound pollution has become a significant global environmental concern. This study examines the spatial and temporal patterns of global PM2.5-O3 compound pollution and exposure risks, firstly at the global and urban scale, using spatial statistical regression, exposure risk assessment, and trend analyses based on the datasets of daily PM2.5 and surface O3 concentrations monitored in 120 cities around the world from 2019 to 2022. Additionally, on the basis of the common emission sources, spatial heterogeneity, interacting chemical mechanisms, and synergistic exposure risk levels between PM2.5 and O3 pollution, we proposed a synergistic PM2.5-O3 control framework for the joint control of PM2.5 and O3. The results indicated that: (1) Nearly 50% of cities worldwide were affected by PM2.5-O3 compound pollution, with China, South Korea, Japan, and India being the global hotspots for PM2.5-O3 compound pollution; (2) Cities with PM2.5-O3 compound pollution have exposure risk levels dominated by ST + ST (Stabilization) and ST + HR (High Risk). Exposure risk levels of compound pollution in developing countries are significantly higher than those in developed countries, with unequal exposure characteristics; (3) The selected cities showed significant positive spatial correlations between PM2.5 and O3 concentrations, which were consistent with the spatial distribution of the precursors NOx and VOCs; (4) During the study period, 52.5% of cities worldwide achieved synergistic reductions in annual average PM2.5 and O3 concentrations. The average PM2.5 concentration in these cities decreased by 13.97%, while the average O3 concentration decreased by 19.18%. This new solution offers the opportunity to construct intelligent and healthy cities in the upcoming low-carbon transition.

Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning

A scientifically sound integrated assessment modeling (IAM) system capable of providing optimized cost-benefit analysis is essential in effective air quality management and control strategy development. Yet scenario optimization for large-scale applications is limited by the computational expense of optimization over many control factors. In this study, a multi-pollutant cost-benefit optimization system based on a genetic algorithm (GA) in machine learning has been developed to provide cost-effective air quality control strategies for large-scale applications (e.g., solution spaces of ~1035). The method was demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for fine particulate matter (PM2.5) and ozone (O3) over the Pearl River Delta (PRD) region of China. The GA was found to be >99% more efficient than the commonly used grid searching method while providing the same combination of optimized multi-pollutant control strategies. The GA method can therefore address air quality management problems that are intractable using the grid searching method. The annual attainment goals for PM2.5 (< 35 μg m-3) and O3 (< 80 ppb) can be achieved simultaneously over the PRD region and surrounding areas by reducing NOx (22%), volatile organic compounds (VOCs, 12%), and primary PM (30%) emissions. However, to attain stricter PM2.5 goals, SO2 reductions (> 9%) are needed as well. The estimated benefit-to-cost ratio of the optimal control strategy reached 17.7 in our application, demonstrating the value of multi-pollutant control for cost-effective air quality management in the PRD region.

Air quality accountability: Developing long-term daily time series of pollutant changes and uncertainties in Atlanta, Georgia resulting from the 1990 Clean Air Act Amendments

The 1990 Clean Air Act Amendments codified major institutional changes relating to the management of air pollutants in the United States. Recent research years has attributed reduced emissions over the past two decades to regulations enacted under these Amendments, but none have separated long-term daily impacts of individual regulatory programs on multiple source categories under a consistent framework. Using daily emissions and air quality measurements along with a detailed review of national and local regulations promulgated after the Amendments, we quantify daily changes in emissions and air quality attributable to regulations on electricity generating units and on-road mobile sources. To quantify daily changes, we develop nine sets of counterfactual emissions and ambient air pollution concentration time series for 10 pollutants that assume individual regulatory programs and combinations thereof were not implemented. In addition to daily impacts, we estimate uncertainties in these results. These counterfactual daily ambient concentrations reveal high seasonality and increasing effectiveness of most regulations between 1999 and 2013. Monthly average counterfactual concentrations in scenarios that assume no new regulations on electricity generating units and mobile sources are greater than observed concentrations for all pollutants except ozone, which has seen increased wintertime concentrations accompany summertime decreases. By the end of the period, electricity generating unit emissions reductions under the Acid Rain Program and Clean Air Interstate Rule and their respective related local programs led to similar PM_2.5 concentration decreases. Of the mobile source regulations, rules on gasoline and diesel vehicles led to similar reductions in annual PM_2.5, and gasoline programs led to double the summertime ozone reductions as diesel programs. The nine sets of daily time series and their uncertainties were designed for use in air pollution accountability health studies.

Effects of BTEX on the Removal of Acetone in a Coaxial Non-Thermal Plasma Reactor: Role Analysis of the Methyl Group

The removal of acetone and benzene series (BTEX) under individual and concurrent conditions is carried out in a coaxial nonthermal plasma (NTP) reactor. The results show that the benzene series has a significant negative impact on acetone conversion and CO₂ selectivity under NTP treatment. Furthermore, it is found that p-xylene significantly promotes CO_x selectivity under co-treatment with acetone because of greater CO generation. Based on the results of transient FTIR, MS, and GC-MS, it is seen that quantities of formic acid, formaldehyde, and ring-opening byproducts from benzene series decomposition are reduced, while quantities of aromatic byproducts with carboxyl, phenolic, and aldehyde groups on the benzene ring increase under coexistence conditions. With the help of theoretical calculation and kinetic analysis, hydrogen abstraction from the methyl group and active hydroxyl radical consumption are proposed as critical factors in the BTEX inhibition effect on acetone degradation.

Resource allocation for mitigating regional air pollution-related mortality: A summertime case study for five cities in the United States

An important issue of regional air quality management is to allocate air quality management funds to maximize environmental and human health benefits. In this study, we use an innovative approach to tackle this air quality management issue. We develop an innovative resource allocation model that allows identification of air pollutant emission control strategies that maximize mortality avoidances subject to a resource constraint. We first present the development of the resource allocation model and then a case study to show how the model can be used to identify resource allocation strategies that maximize mortality avoidances for top five Metropolitan Statistical Areas (MSAs) (i.e., New York, Los Angeles, Chicago, Dallas-Fort Worth, and Philadelphia) in the continental United States collectively. Given budget constraints in the U.S. Environmental Protection Agency's (EPA) Clean Air Act assessment, the results of the case study suggest that controls of sulfur dioxide (SO2) and primary carbon (PC) emissions from EPA Regions 2, 3, 5, 6, and 9 would have significant health benefits for the five selected cities collectively. Around 30,800 air pollution-related mortalities could be avoided during the selected 2-week summertime episode for the five cities collectively if the budget could be allocated based on the results of the resource allocation model. Although only five U.S. cities during a 2-week episode are considered in the case study, the resource allocation model can be used by decision-makers to plan air pollution mitigation strategies to achieve the most significant health benefits for other seasons and more cities over a region or the continental U.S.

IMPLICATIONS

Effective allocations of air quality management resources are challenging and complicated, and it is desired to have a tool that can help decision-makers better allocate the funds to maximize health benefits of air pollution mitigation. An innovative resource allocation model developed in this study can help decision-makers identify the best resource allocation strategies for multiple cities collectively. The results of a case study suggest that controls of primary carbon and sulfur dioxides emissions would achieve the most significant health benefits for five selected cities collectively.

Optimization of multipollutant air quality management strategies: A case study for five cities in the United States

Developing regional air quality management strategies is a difficult task because formation of air pollutants is interdependent and air quality at different locations may have different responses to emissions from common sources. We developed an optimization-based model, OPtimal integrated Emission Reduction Alternatives (OPERA), which allows for identifications of least-cost control strategies for attaining multipollutant air quality targets at multiple locations simultaneously. To implement OPERA, first, sensitivities of air quality to precursor emission changes are quantified. Second, cost functions of emission reductions are estimated using a cost analysis tool that includes a pool of available control measures. The third step is to determine desired reductions in concentrations of air pollutants. The last step is to identify the optimal control strategies by minimizing costs of emission controls using the sensitivities of air pollutants to emission changes, cost functions, and constraints for feasible emission reduction ratios. A case study that investigates ozone and PM2.5 air quality in the summer of 2007 for five major cities in the eastern United States is presented in this paper. The results of the OPERA calculations show that reductions in regional NOx and VOC as well as local primary PM2.5 emissions were more cost-effective than SO2 controls for decreasing ozone and total PM2.5 concentrations in the summer of 2007. This was because reductions in SO2 emissions would only decrease PM2.5 concentrations, and reductions in primary PM2.5 emissions were more cost-effective than SO2 emission controls.

IMPLICATIONS

We developed an optimization-based model, OPtimal integrated Emission Reduction Alternatives (OPERA), which allows for identification of least-cost emission control strategies for attaining multipollutant air quality targets at multiple locations simultaneously. A major strength of OPERA is its flexibility, which allows for changes in air quality regulations, involving agencies, study regions, and so on, to be readily incorporated. Overall, it has been demonstrated that OPERA is useful in developing least-cost emission control strategies for achieving multipollutant air quality targets at multiple locations simultaneously and could be useful for policymakers developing integrated air quality management plans.

Synthesizing scientific progress: outcomes from U.S. EPA's carbonaceous aerosols and source apportionment STAR grants

In response to recommendations by the National Research Council in the late 1990 s and early 2000s for critical research into understanding sources and formation mechanisms of PM2.5, EPA created multiple funding opportunities through the Science to Achieve Results (STAR) program: "Measurement, Modeling, and Analysis Methods for Airborne Carbonaceous Fine Particulate Matter" (2003) and "Source Apportionment of Particulate Matter" (2004). The carbonaceous fine PM solicitation resulted in 16 different projects focusing on the measurement methods, source identification, and exploration of the chemical and physical processes important for PM2.5 carbon in the atmosphere. The source apportionment funding opportunity led to 11 projects improving tools and characterization of source-receptor relationships of PM2.5. Many funding mechanisms include a final synopsis of funded research and published manuscripts. Here, this evaluation is extended to include citations of research published as part of these solicitations. These solicitations resulted in 275 publications that included more than 850 unique authors in 37 different journals with a weighted average 2011 impact factor of 4.21. At the time of this assessment, these publications have been cited by 13,612 peer review journal articles with 31 (11%) of the manuscripts being cited over 100 times.

Ozone sensitivity to its precursor emissions in northeastern Mexico for a summer air pollution episode

A summer episode was modeled to address the expected response of ambient air O3 to hypothetical emission control scenarios in northeastern Mexico, and in particular in the Monterrey Metropolitan Area (MMA). This region is of interest because the MMA holds one of the worst air quality problems in the country and levels of air pollutants in the rest of northeastern Mexico are starting to be a concern. The MM5-SMOKE-CMAQ platform was used to conduct the numerical experiments. Twenty-four control scenarios were evaluated, combining the level of emission controls of O3 precursors (NO(x) and volatile organic compounds [VOCs]) from 0% to 50%. For the MMA, VOC-only controls result in the best option to reduce O3 concentrations, though the benefit is limited to the urban core. This same strategy results in negligible benefits for the rest of northeastern Mexico. NO(x) controls result in an increase in O3 concentration within the MMA of up to 20 ppbv and a decrease at downwind locations of up to 11 ppbv, with respect to the base-case scenario. Indicator ratios were also used to probe for NO(x)-sensitive and VOC-sensitive areas. Locations with an important influence of NO(x) point sources (i.e., Monclova and Nava/Acuña) are quite sensitive to changes in NO(x) emissions. Border cities in the Rio Bravo/Grande Valley tend to be marginally NO(x)-sensitive. Overall, the MMA seems to be dominated by a VOC-sensitive regime, while the rest of the region would tend to have a NO(x)-sensitive response. The results obtained serve to expand the current knowledge on the chemical regimes that dominate this region (VOC- or NO(x)-sensitive), and thus could help guide public policies related to emission regional control strategies.

Development of risk-based air quality management strategies under impacts of climate change

Climate change is forecast to adversely affect air quality through perturbations in meteorological conditions, photochemical reactions, and precursor emissions. To protect the environment and human health from air pollution, there is an increasing recognition of the necessity of developing effective air quality management strategies under the impacts of climate change. This paper presents a framework for developing risk-based air quality management strategies that can help policy makers improve their decision-making processes in response to current and future climate change about 30-50 years from now. Development of air quality management strategies under the impacts of climate change is fundamentally a risk assessment and risk management process involving four steps: (1) assessment of the impacts of climate change and associated uncertainties; (2) determination of air quality targets; (3) selections of potential air quality management options; and (4) identification of preferred air quality management strategies that minimize control costs, maximize benefits, or limit the adverse effects of climate change on air quality when considering the scarcity of resources. The main challenge relates to the level of uncertainties associated with climate change forecasts and advancements in future control measures, since they will significantly affect the risk assessment results and development of effective air quality management plans. The concept presented in this paper can help decision makers make appropriate responses to climate change, since it provides an integrated approach for climate risk assessment and management when developing air quality management strategies.

IMPLICATIONS

Development of climate-responsive air quality management strategies is fundamentally a risk assessment and risk management process. The risk assessment process includes quantification of climate change impacts on air quality and associated uncertainties. Risk management for air quality under the impacts of climate change includes determination of air quality targets, selections of potential management options, and identification of effective air quality management strategies through decision-making models. The risk-based decision-making framework can also be applied to develop climate-responsive management strategies for the other environmental dimensions and assess costs and benefits of future environmental management policies.

Using backup generators for meeting peak electricity demand: a sensitivity analysis on emission controls, location, and health endpoints

Generators installed for backup power during blackouts could help satisfy peak electricity demand; however, many are diesel generators with nonnegligible air emissions that may damage air quality and human health. The full (private and social) cost of using diesel generators with and without emission control retrofits for fine particulate matter (PM2.5) and nitrogen oxides (NOx) were compared with a new natural gas turbine peaking plant. Lower private costs were found for the backup generators because the capital costs are mostly ascribed to reliability. To estimate the social costs from air quality, the changes in ambient concentrations of ozone (O3) and PM2.5 were modeled using the Particulate Matter Comprehensive Air Quality Model with extensions (PMCAMx) chemical transport model. These air quality changes were translated to their equivalent human health effects using concentration-response functions and then into dollars using estimates of "willingness-to-pay" to avoid ill health. As a case study, 1000 MW of backup generation operating for 12 hr/day for 6 days in each of four eastern U.S. cities (Atlanta, Chicago, Dallas, and New York) was modeled. In all cities, modeled PM2.5 concentrations increased (up to 5 microg/m3) due mainly to primary emissions. Smaller increases and decreases were observed for secondary PM2.5 with more variation between cities. Increases in NOx, emissions resulted in significant nitrate formation (up to 1 microg/m3) in Atlanta and Chicago. The NOx emissions also caused O3 decreases in the urban centers and increases in the surrounding areas. For PM2.5, a social cost of approximately $2/kWh was calculated for uncontrolled diesel generators in highly populated cities but was under 10 cent/kWh with PM2.5 and NOx controls. On a full cost basis, it was found that properly controlled diesel generators are cost-effective for meeting peak electricity demand. The authors recommend NOx and PM2.5 controls.