Synergistic effects of copper and oxygen vacancies in enhancing the efficacy of partially crystalline CuMnxOy catalyst for ozone decomposition

To tackle the challenge of ground-level ozone pollution, this study proposed a potential catalytic design approach for ozone decomposition using Cu-Mn bimetallic oxide. This approach is grounded in an understanding of the intrinsic reactivity for catalyst and incorporates a novel potassium-driven low-temperature oxidation process for catalyst synthesis. The research highlights the creation of a highly reactive Cu-Mn oxide phase with extensive defect coverage, leading to significantly increased reaction rates. It also identifies the MnO2(100) facet as a crucial active phase, where oxygen vacancies simultaneously enhance O3 adsorption and decomposition, albeit with a concurrent risk of O2 poisoning due to the stabilization of adsorbed O2. Crucially, the incorporation of Cu offsets the effects of oxygen vacancies, influencing conversion rates and lessening O2 poisoning. The synergistic interplay between Cu and oxygen vacancies elevates the performance of the defect-rich Cu-Mn oxide catalyst. By combining computational and experimental methods, this study not only advances the understanding of the Cu-Mn oxide system for ozone decomposition but also contributes valuable insights into developing more efficient catalysts to mitigate ozone pollution.

Downscaling of environmental indicators: A review

Environmental indicators at different scales are important for environmental management, daily life, and scientific research. Because of the lack of statistics below a national scale for many environmental indicators, scholars have developed various downscaling methods to obtain finer-scale and diverse forms of data for different environmental indicators. However, the existing downscaling methods for environmental indicators are diverse and fragmented. Here, we reviewed the downscaling methods by reclassifying the environmental indicators from a life cycle perspective into five categories: natural resources use and related attributes; material and energy consumption; environmental discharge; climate change; and environmental footprints. We first provide a general introduction to downscaling theory in the environmental field, including definitions, techniques, and evolution. We then elaborate on downscaling methods and make an inventory of the five categories of environmental indicators. We summarize the downscaling methods commonly applied to specific indicators, scale transformation, the strengths and limitations of corresponding methods, and provide specific examples. Next, we discuss ways to select or construct downscaling methods based on four principles: objective orientation, data accessibility, model feasibility, and model adjustment. Finally, we explore the future direction of downscaling and provide insights for improving downscaling for environmental indicators. In this review, we generalize and clarify the downscaling techniques for environmental indicators, which will help facilitate the appropriate selection of downscaling methods by researchers.

Impacts of climate change on the fate of contaminants through extreme weather events

The direct impacts of climate change involve a multitude of phenomena, including rising sea levels, intensified severe weather events such as droughts and flooding, increased temperatures leading to wildfires, and unpredictable fluctuations in rainfall. This comprehensive review intends to examine firstly the probable consequences of climate change on extreme weather events such as drought, flood and wildfire. This review subsequently examines the release and transformation of contaminants in terrestrial, aquatic, and atmospheric environments in response to extreme weather events driven by climate change. While drought and flood influence the dynamics of inorganic and organic contaminants in terrestrial and aquatic environments, thereby influencing their mobility and transport, wildfire results in the release and spread of organic contaminants in the atmosphere. There is a nascent awareness of climate change's influence of climate change-induced extreme weather events on the dynamics of environmental contaminants in the scientific community and decision-making processes. The remediation industry, in particular, lags behind in adopting adaptive measures for managing contaminated environments affected by climate change-induced extreme weather events. However, recognizing the need for assessment measures represents a pivotal first step towards fostering more adaptive practices in the management of contaminated environments. We highlight the urgency of collaboration between environmental chemists and climate change experts, emphasizing the importance of jointly assessing the fate of contaminants and rigorous action to augment risk assessment and remediation strategies to safeguard the health of our environment.

A method for calibrating measurement data of a micro air quality monitor based on MLR-BRT-ARIMA combined model

A micro air quality monitor can realize grid monitoring and real-time monitoring of air pollutants. Its development can effectively help human beings to control air pollution and improve air quality. However, affected by many factors, the measurement accuracy of micro air quality monitors needs to be improved. In this paper, a combined calibration model of Multiple Linear Regression, Boosted Regression Tree and AutoRegressive Integrated Moving Average model (MLR-BRT-ARIMA) is proposed to calibrate the measurement data of the micro air quality monitor. First, the very widely used and easily interpretable multiple linear regression model is used to find the linear relationship between various pollutant concentrations and the measurement data of the micro air quality monitor to obtain the fitted values of various pollutant concentrations. Second, we take the measurement data of the micro air quality monitor and the fitted value of the multiple regression model as the input, and use the boosted regression tree to find the nonlinear relationship between the concentrations of various pollutants and the input variables. Finally, the autoregressive integrated moving average model is used to extract the information hidden in the residual sequence, and finally the establishment of the MLR-BRT-ARIMA model is completed. Root mean square error, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of the MLR-BRT-ARIMA model and other commonly used models such as multilayer perceptron neural network, support vector regression machine and nonlinear autoregressive models with exogenous input. The results show that no matter what kind of pollutant, the MLR-BRT-ARIMA combined model proposed in this paper has the best performance of the three indicators. Using this model to calibrate the measurement value of the micro air quality monitor can improve the accuracy by 82.4-95.4%.

High time-resolution source apportionment and health risk assessment for PM2.5-bound elements at an industrial city in northwest China

To better respond to heavy air pollution, the local government of Baoji City, a traditionally industry dominated city in northwest China, released several warning levels between December 2019 and January 2020. The system aims to provide a more efficient control of pollution sources. In this study, a high-time resolution measurement of PM_2.5-bound elements was applied to capture the diurnal-scale dynamic processes associated with major pollution activities in northwest China. A series of elements were quantified and used for source apportionment using the positive matrix factorization (PMF) model. Combined with the local characteristics, nine sources were resolved with contributions in descending order: fugitive dust (36.6 %), biomass burning (20.1 %), traffic-related (10.4 %), coal combustion (10.0 %), titanium alloy smelting (7.2 %), As-related industry (6.9 %), Zn-related industry (5.6 %), molybdenum alloy smelting (2.5 %), and Cr-related industry (0.7 %). The health risk assessment indicated non-carcinogenic risks for Mn and carcinogenic risks for As and Cr in both adults and children. The cumulative non-carcinogenic risk for the elements was 3.2 times the safety threshold, while the carcinogenic risk (CR) was 6.8 and 27 times the acceptable levels for children and adults, respectively. For source-resolved risks, As- and Cr-related industry emissions showed the highest carcinogenic risk. Five of the nine resolved sources for adults have CR values 1.4 and 9.7 times the acceptable level. This study provides valuable information for developing targeted strategies to control air pollutants and protect public health.

GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms

Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.

Estimating lightning NOx production over South Africa

Nitrogen oxides (NOx = NO + NO2) are toxic air pollutants and play a significant role in tropospheric chemistry. Global NOx hotspots are the industrialised regions of the USA, Europe, Middle East, East Asia and eastern parts of South Africa. Lightning is one of the many natural and anthropogenic sources of NOx to the troposphere. It plays a role in the formation of particulate matter and tropospheric ozone, which are both linked to harmful health and climate effects. The discourse on NOx over the southern African continent has mainly focused on anthropogenic sources. However, lightning is known to be a main source of tropospheric NOx globally. It is therefore important to understand its contribution to the national and global NOx budget. Data from the South African Lightning Detection Network were used to approximate the influence of lightning on the NOx load over the country, and to develop a gridded data set of lightning-produced NOx (LNOx) emissions for the period 2008 2015. The Network monitors cloud-toground lightning strikes; and theoretically has a detection efficiency of 90% and a location accuracy of 0.5 km. An emission factor of 11.5 kg NO2/flash was employed to calculate the LNOx budget of ~270 kt NO2/year. The calculated LNOx was 14% of the total NOx emission estimates published in the EDGAR v4.2 data set for the year 2008. The LNOx emission inventory will improve model performance and prediction, and enhance the understanding of the contribution of lightning to ambient NO2.

Crop Residue Burning in Northeast China and Its Impact on PM2.5 Concentrations in South Korea

The impact of crop residue burning in northeastern China on South Korean PM2.5 concentrations was assessed via weather conditions, air quality modeling (AQM), and PM2.5 composition data during two cases exceeding 35 µg·m−3 in November 2015. PM2.5 concentration simulations of Case 1 differed from observations by 3.7–17.6 µg·m−3, overestimating the levels by 6–36%; however, Case 2 varied by 20.0–59.8 µg·m−3 from observations, with a 53–91% underestimation. Case 1 was generally well simulated, whereas the Case 2 simulation failed because the emissions of crop residue burning in northeastern China, as confirmed through satellite analysis (MODIS fires and thermal anomalies) and previous research, were not considered. The portion of organic/elemental carbon ratio during Case 2 was 1.6–2.3 times higher than that of Case 1. These results suggest that it is necessary to consider the effects of crop residue burning in northeast China to establish countermeasures to improve air quality and air quality forecasting in South Korea.

Estimated Mortality and Morbidity Attributable to Smoke Plumes in the United States: Not Just a Western US Problem

As anthropogenic emissions continue to decline and emissions from landscape (wild, prescribed, and agricultural) fires increase across the coming century, the relative importance of landscape-fire smoke on air quality and health in the United States (US) will increase. Landscape fires are a large source of fine particulate matter (PM2.5), which has known negative impacts on human health. The seasonal and spatial distribution, particle composition, and co-emitted species in landscape-fire emissions are different from anthropogenic sources of PM2.5. The implications of landscape-fire emissions on the sub-national temporal and spatial distribution of health events and the relative health importance of specific pollutants within smoke are not well understood. We use a health impact assessment with observation-based smoke PM2.5 to determine the sub-national distribution of mortality and the sub-national and sub-annual distribution of asthma morbidity attributable to US smoke PM2.5 from 2006 to 2018. We estimate disability-adjusted life years (DALYs) for PM2.5 and 18 gas-phase hazardous air pollutants (HAPs) in smoke. Although the majority of large landscape fires occur in the western US, we find the majority of mortality (74%) and asthma morbidity (on average 75% across 2006-2018) attributable to smoke PM2.5 occurs outside the West, due to higher population density in the East. Across the US, smoke-attributable asthma morbidity predominantly occurs in spring and summer. The number of DALYs associated with smoke PM2.5 is approximately three orders of magnitude higher than DALYs associated with gas-phase smoke HAPs. Our results indicate awareness and mitigation of landscape-fire smoke exposure is important across the US.

Predicting U.S. Residential Building Energy Use and Indoor Pollutant Exposures in the Mid-21st Century

The extent to which climate change and other factors will influence building energy use and population exposures to indoor pollutants is not well understood. Here, we develop and apply nationally representative residential energy and indoor pollutant model sets to estimate energy use, indoor pollutant concentrations, and associated chronic health outcomes across the U.S. residential building stock in the mid-21st century. The models incorporate expected changes in meteorological and ambient air quality conditions associated with IPCC RCP 8.5 and assumptions for changes in housing characteristics and population movements while keeping other less predictable factors constant. Site and source energy consumption for residential space-conditioning are predicted to decrease by ∼37-43 and ∼20-31%, respectively, in the 2050s compared to those in a 2010s reference scenario. Population-average indoor concentrations of pollutants of ambient origin are expected to decrease, except for O₃. Holding indoor emission factors constant, indoor concentrations of pollutants with intermittent indoor sources are expected to decrease by <5% (PM_2.5) to >30% (NO₂); indoor concentrations of pollutants with persistent indoor sources (e.g., volatile organic compounds (VOCs)) are predicted to increase by ∼15-45%. We estimate negligible changes in disability-adjusted life-years (DALYs) lost associated with residential indoor pollutant exposures, well within uncertainty, although the attribution among pollutants is predicted to vary.

Teleconnection between the Asian Polar Vortex and surface PM2.5 in China

Atmospheric fine particulate matter (PM_2.5) pollutions are of particular concern because of their direct and indirect harm to humans and organisms. China has suffered from severe air pollution for the past ten years, related to heavy pollution emissions and compounded by the effects of atmospheric circulation. This study applied statistical methods, observational data of ground pollutants, and meteorological data to analyze the impact of large-scale atmospheric circulations on PM_2.5 pollution over China. Empirical orthogonal function (EOF) analysis was used to evaluate the main PM_2.5 patterns and total contributions of the leading four EOFs. The results indicate that the total contributions of the leading four EOFs accounted for 50.5% of the total variance, reflecting four main types of PM_2.5 pollution, namely, overall pollution phase, north–south phase, east–west phase and north–center–south phase, with contributions of 28.4%, 9.7%, 6.5% and 5.9%, respectively. We selected indices of the Asian Polar Vortex (APV) to analyze the impact of large-scale atmospheric circulations on PM_2.5 pollution over China. The most pronounced APV control occurred in Beijing and its surroundings, specifically, along the Bohai Sea and the Northeast Plain.

Impact of urban heat island on meteorology and air quality at microenvironments

This study analyzes the air pollution characteristics and their relation to meteorological conditions in Chennai, India. Meteorological conditions were the primary factor determining variations in daily average pollutant concentrations. The influence of urban infrastructure on meteorology is an important prediction on air quality. Understanding of the seasonal and diurnal secondary pollutant concentrations as a function of local meteorological conditions is necessary for urban air quality management. Micro-scale models for analyzing the surface layer interactions with the surrounding environment have recently gained attention. An attempt has been made to understand the effect of meteorology on air quality. This comprehensive study aims to assess the influence of local meteorology on urban air quality. The correlation was established between the change in meteorological parameters and mixing height on air quality at selected locations in a tropical urban environment. Results indicated the significant impact of land use patterns on the dispersion of air quality at study locations. Seasonal variations of ambient air temperatures at study locations were found to be more than 3°C in summer. Average mixing height variation among the study locations was observed to be more than 200 meters in summer. Results indicated the importance of wind velocity on the mixing height at study locations. The average concentrations of air quality parameters showed significant variation among the study locations. The maximum ozone (O₃) concentration was recorded at the Central Business District (CBD) during the afternoon, i.e., around 38.3 ppb, whereas it was 26.8 and 14.6 ppb at the Residential Area (RA) and Urban Baseline (UBL), respectively. A strong correlation was observed between ambient temperature and O₃ concentration during summer. In the winter, the average O₃ concentration in all three-study locations increased to 45.3 ppb, 45.8 ppb, and 58.5 ppb at UBL, RA, and CBD sites, respectively. The study reveals the impact of microenvironments on air quality. Implications: An attempt has been made to study the seasonal and diurnal variation of air quality levels in selected study regions with land cover change. This article focuses mainly on the surface temperature intensity variations with respect to the percentage of land use pattern change in Chennai city, India, and the subsequent effect on meteorology of dispersion conditions and air quality parameters has been studied. The relationship between local meteorology and air quality has been established.

Modeling the Effects of Climate Change on Surface Ozone during Summer in the Yangtze River Delta Region, China

Future climate change can impact ozone concentrations by changing regional meteorological factors related to ozone (O3) pollution. To better understand the variations of meteorological factors and their effects on O3 formation processes under future climate conditions, we model the present and the future meteorology and air quality in summer over the Yangtze River Delta (YRD) region by using the Weather Research and Forecasting Model with Chemistry module (WRF/Chem), which is driven by the outputs of Community Climate System Model version 4 (CCSM4). The simulations predict that solar radiation, 2-m air temperature, and wind speed increase in the daytime over most of the YRD region. Absolute humidity and precipitation increase in the north and decrease in the south, while the planetary boundary layer height (PBLH) has an opposite change pattern displaying a decrease in the north and an increase in the south. The southerly wind will be strengthened in the daytime. At night, the change patterns of the meteorological factors are similar to the daytime but with small variations. Meanwhile, O3 and its precursors all increase in the north and decrease in the south. The increases of NOx, volatile organic compounds (VOC), and CO are related with the decreases of PBLH and the input effect of stronger southerly wind, while the decreases are attributed to the output effect of the stronger southerly wind. During the daytime, the increase of surface O3 in the north is dominated by the chemical processes related with the increases of solar radiation, air temperature, and O3 precursors. The decrease of surface O3 in the south is mainly caused by the transport process changing with the strengthened southerly wind. At night, the surface O3 changing the amplitude is less than the daytime. The less O3 variations at night can be attributed to an O3 titration reaction with NO, the changes in NOx concentrations, and the increases of nocturnal PBLH. With the aid of H2O2/HNO3, O3 formation in the YRD region is found to be easily affected by NOx in the future. The findings can help to understand the changing trend of O3 in the YRD region and can propose reasonable pollution control policies.

The Exposome and Asthma

This article on exposome and asthma focuses on the interaction of patients and their environments in various parts of their growth, development, and stages of life. Indoor and outdoor environments play a role in pathogenesis via levels and duration of exposure, with genetic susceptibility as a crucial factor that alters the initiation and trajectory of common conditions such as asthma. Knowledge of environmental exposures globally and changes that are occurring is necessary to function effectively as medical professionals and health advocates.

Air-quality-related health impacts from climate change and from adaptation of cooling demand for buildings in the eastern United States: An interdisciplinary modeling study

BACKGROUND

Climate change negatively impacts human health through heat stress and exposure to worsened air pollution, amongst other pathways. Indoor use of air conditioning can be an effective strategy to reduce heat exposure. However, increased air conditioning use increases emissions of air pollutants from power plants, in turn worsening air quality and human health impacts. We used an interdisciplinary linked model system to quantify the impacts of heat-driven adaptation through building cooling demand on air-quality-related health outcomes in a representative mid-century climate scenario.

METHODS AND FINDINGS

We used a modeling system that included downscaling historical and future climate data with the Weather Research and Forecasting (WRF) model, simulating building electricity demand using the Regional Building Energy Simulation System (RBESS), simulating power sector production and emissions using MyPower, simulating ambient air quality using the Community Multiscale Air Quality (CMAQ) model, and calculating the incidence of adverse health outcomes using the Environmental Benefits Mapping and Analysis Program (BenMAP). We performed simulations for a representative present-day climate scenario and 2 representative mid-century climate scenarios, with and without exacerbated power sector emissions from adaptation in building energy use. We find that by mid-century, climate change alone can increase fine particulate matter (PM2.5) concentrations by 58.6% (2.50 μg/m3) and ozone (O3) by 14.9% (8.06 parts per billion by volume [ppbv]) for the month of July. A larger change is found when comparing the present day to the combined impact of climate change and increased building energy use, where PM2.5 increases 61.1% (2.60 μg/m3) and O3 increases 15.9% (8.64 ppbv). Therefore, 3.8% of the total increase in PM2.5 and 6.7% of the total increase in O3 is attributable to adaptive behavior (extra air conditioning use). Health impacts assessment finds that for a mid-century climate change scenario (with adaptation), annual PM2.5-related adult mortality increases by 13,547 deaths (14 concentration-response functions with mean incidence range of 1,320 to 26,481, approximately US$126 billion cost) and annual O3-related adult mortality increases by 3,514 deaths (3 functions with mean incidence range of 2,175 to 4,920, approximately US$32.5 billion cost), calculated as a 3-month summer estimate based on July modeling. Air conditioning adaptation accounts for 654 (range of 87 to 1,245) of the PM2.5-related deaths (approximately US$6 billion cost, a 4.8% increase above climate change impacts alone) and 315 (range of 198 to 438) of the O3-related deaths (approximately US$3 billion cost, an 8.7% increase above climate change impacts alone). Limitations of this study include modeling only a single month, based on 1 model-year of future climate simulations. As a result, we do not project the future, but rather describe the potential damages from interactions arising between climate, energy use, and air quality.

CONCLUSIONS

This study examines the contribution of future air-pollution-related health damages that are caused by the power sector through heat-driven air conditioning adaptation in buildings. Results show that without intervention, approximately 5%-9% of exacerbated air-pollution-related mortality will be due to increases in power sector emissions from heat-driven building electricity demand. This analysis highlights the need for cleaner energy sources, energy efficiency, and energy conservation to meet our growing dependence on building cooling systems and simultaneously mitigate climate change.

Future ozone-related acute excess mortality under climate and population change scenarios in China: A modeling study

BACKGROUND

Climate change is likely to further worsen ozone pollution in already heavily polluted areas, leading to increased ozone-related health burdens. However, little evidence exists in China, the world's largest greenhouse gas emitter and most populated country. As China is embracing an aging population with changing population size and falling age-standardized mortality rates, the potential impact of population change on ozone-related health burdens is unclear. Moreover, little is known about the seasonal variation of ozone-related health burdens under climate change. We aimed to assess near-term (mid-21st century) future annual and seasonal excess mortality from short-term exposure to ambient ozone in 104 Chinese cities under 2 climate and emission change scenarios and 6 population change scenarios.

METHODS AND FINDINGS

We collected historical ambient ozone observations, population change projections, and baseline mortality rates in 104 cities across China during April 27, 2013, to October 31, 2015 (2013-2015), which included approximately 13% of the total population of mainland China. Using historical ozone monitoring data, we performed bias correction and spatially downscaled future ozone projections at a coarse spatial resolution (2.0° × 2.5°) for the period April 27, 2053, to October 31, 2055 (2053-2055), from a global chemistry-climate model to a fine spatial resolution (0.25° × 0.25°) under 2 Intergovernmental Panel on Climate Change Representative Concentration Pathways (RCPs): RCP4.5, a moderate global warming and emission scenario where global warming is between 1.5°C and 2.0°C, and RCP8.5, a high global warming and emission scenario where global warming exceeds 2.0°C. We then estimated the future annual and seasonal ozone-related acute excess mortality attributable to both climate and population changes using cause-specific, age-group-specific, and season-specific concentration-response functions (CRFs). We used Monte Carlo simulations to obtain empirical confidence intervals (eCIs), quantifying the uncertainty in CRFs and the variability across ensemble members (i.e., 3 predictions of future climate and air quality from slightly different starting conditions) of the global model. Estimates of future changes in annual ozone-related mortality are sensitive to the choice of global warming and emission scenario, decreasing under RCP4.5 (-24.0%) due to declining ozone precursor emissions but increasing under RCP8.5 (10.7%) due to warming climate in 2053-2055 relative to 2013-2015. Higher ambient ozone occurs under the high global warming and emission scenario (RCP8.5), leading to an excess 1,476 (95% eCI: 898 to 2,977) non-accidental deaths per year in 2053-2055 relative to 2013-2015. Future ozone-related acute excess mortality from cardiovascular diseases was 5-8 times greater than that from respiratory diseases. Ozone concentrations increase by 15.1 parts per billion (10-9) in colder months (November to April), contributing to a net yearly increase of 22.3% (95% eCI: 7.7% to 35.4%) in ozone-related mortality under RCP8.5. An aging population, with the proportion of the population aged 65 years and above increased from 8% in 2010 to 24%-33% in 2050, will substantially amplify future ozone-related mortality, leading to a net increase of 23,838 to 78,560 deaths (110% to 363%). Our analysis was mainly limited by using a single global chemistry-climate model and the statistical downscaling approach to project ozone changes under climate change.

CONCLUSIONS

Our analysis shows increased future ozone-related acute excess mortality under the high global warming and emission scenario RCP8.5 for an aging population in China. Comparison with the lower global warming and emission scenario RCP4.5 suggests that climate change mitigation measures are needed to prevent a rising health burden from exposure to ambient ozone pollution in China.

Air pollution at Rochester, NY: Long-term trends and multivariate analysis of upwind SO2 source impacts

There have been many changes in the air pollutant sources in the northeastern United States since 2001. To assess the effect of these changes, trend analyses of the monthly average values were performed on PM_2.5 and its components including major ions, elemental carbon (EC), organic carbon (OC), and gaseous pollutant concentrations measured between 2001 (in some cases 1999) and 2015 at the NYS Department of Environmental Conservation sites in Rochester, NY. Mann-Kendall regression with Sen's slope was applied to estimate the trends and seasonality. Using piecewise regression, significant reductions in the air pollution of Rochester area were observed between 2008 and 2010 when a 260MW coal-fired power plant was decommissioned, new heavy-duty diesel trucks had to be equipped with catalytic regenerator traps, and the economic recession that began in 2008 reduced traffic and other activities. The monthly average PM_2.5 mass showed a downward trend (-5μg/m³; -41%) in Rochester between 2001 and 2015. This change is largely due to reductions in particulate sulfate that showed a 65% decrease. The sulfate concentrations were compared to changes in SO₂ emissions in seventeen upwind source domains, and other systematic changes by multivariate linear regression. Selectivity ratio obtained from target projection discriminated the most important source domains that are SO₂ emissions from Georgia for winter, North Carolina for transition (spring and fall) and Ohio along with other influences for summer. North Carolina and Michigan were identified as the main sources for entire period. These observations suggest that any further reductions in the specified regional SO₂ emissions would result in a proportional decrease in sulfate in Rochester.

Tropospheric ozone assessment report: Global ozone metrics for climate change, human health, and crop/ecosystem research

Assessment of spatial and temporal variation in the impacts of ozone on human health, vegetation, and climate requires appropriate metrics. A key component of the Tropospheric Ozone Assessment Report (TOAR) is the consistent calculation of these metrics at thousands of monitoring sites globally. Investigating temporal trends in these metrics required that the same statistical methods be applied across these ozone monitoring sites. The nonparametric Mann-Kendall test (for significant trends) and the Theil-Sen estimator (for estimating the magnitude of trend) were selected to provide robust methods across all sites. This paper provides the scientific underpinnings necessary to better understand the implications of and rationale for selecting a specific TOAR metric for assessing spatial and temporal variation in ozone for a particular impact. The rationale and underlying research evidence that influence the derivation of specific metrics are given. The form of 25 metrics (4 for model-measurement comparison, 5 for characterization of ozone in the free troposphere, 11 for human health impacts, and 5 for vegetation impacts) are described. Finally, this study categorizes health and vegetation exposure metrics based on the extent to which they are determined only by the highest hourly ozone levels, or by a wider range of values. The magnitude of the metrics is influenced by both the distribution of hourly average ozone concentrations at a site location, and the extent to which a particular metric is determined by relatively low, moderate, and high hourly ozone levels. Hence, for the same ozone time series, changes in the distribution of ozone concentrations can result in different changes in the magnitude and direction of trends for different metrics. Thus, dissimilar conclusions about the effect of changes in the drivers of ozone variability (e.g., precursor emissions) on health and vegetation exposure can result from the selection of different metrics.

Urban heat island (UHI) influence on secondary pollutant formation in a tropical humid environment

The combined action of urbanization (change in land use) and increase in vehicular emissions intensifies the urban heat island (UHI) effect in many cities in the developed countries. The urban warming (UHI) enhances heat-stress-related diseases and ozone (O₃) levels due to a photochemical reaction. Even though UHI intensity depends on wind speed, wind direction, and solar flux, the thermodynamic properties of surface materials can accelerate the temperature profiles at the local scale. This mechanism modifies the atmospheric boundary layer (ABL) structure and mixing height in urban regions. These changes further deteriorate the local air quality. In this work, an attempt has been made to understand the interrelationship between air pollution and UHI intensity at selected urban areas located at tropical environment. The characteristics of ambient temperature profiles associated with land use changes in the different microenvironments of Chennai city were simulated using the Envi-Met model. The simulated surface 24-hr average air temperatures (11 m above the ground) for urban background and commercial and residential sites were found to be 30.81 ± 2.06, 31.51 ± 1.87, and 31.33 ± 2.1ºC, respectively. The diurnal variation of UHI intensity was determined by comparing the daytime average air temperatures to the diurnal air temperature for different wind velocity conditions. From the model simulations, we found that wind speed of 0.2 to 5 m/sec aggravates the UHI intensity. Further, the diurnal variation of mixing height was also estimated at the study locations. The estimated lowest mixing height at the residential area was found to be 60 m in the middle of night. During the same period, highest ozone (O₃) concentrations were also recorded at the continuous ambient air quality monitoring station (CAAQMS) located at the residential area.

IMPLICATIONS

An attempt has made to study the diurnal variation of secondary pollution levels in different study regions. This paper focuses mainly on the UHI intensity variations with respect to percentage of land use pattern change in Chennai city, India. The study simulated the area-based land use pattern with local mixing height variations. The relationship between UHI intensity and mixing height provides variations on local air quality.

Co-occurrence of extremes in surface ozone, particulate matter, and temperature over eastern North America

Heat waves and air pollution episodes pose a serious threat to human health and may worsen under future climate change. In this paper, we use 15 years (1999-2013) of commensurately gridded (1° x 1°) surface observations of extended summer (April-September) surface ozone (O3), fine particulate matter (PM2.5), and maximum temperature (TX) over the eastern United States and Canada to construct a climatology of the coincidence, overlap, and lag in space and time of their extremes. Extremes of each quantity are defined climatologically at each grid cell as the 50 d with the highest values in three 5-y windows (∼95th percentile). Any two extremes occur on the same day in the same grid cell more than 50% of the time in the northeastern United States, but on a domain average, co-occurrence is approximately 30%. Although not exactly co-occurring, many of these extremes show connectedness with consistent offsets in space and in time, which often defy traditional mechanistic explanations. All three extremes occur primarily in large-scale, multiday, spatially connected episodes with scales of >1,000 km and clearly coincide with large-scale meteorological features. The largest, longest-lived episodes have the highest incidence of co-occurrence and contain extreme values well above their local 95th percentile threshold, by +7 ppb for O3, +6 µg m-3 for PM2.5, and +1.7 °C for TX. Our results demonstrate the need to evaluate these extremes as synergistic costressors to accurately quantify their impacts on human health.

Evaluating the effectiveness of air quality regulations: A review of accountability studies and frameworks

Assessments of past environmental policies-termed accountability studies-contribute important information to the decision-making process used to review the efficacy of past policies, and subsequently aid in the development of effective new policies. These studies have used a variety of methods that have achieved varying levels of success at linking improvements in air quality and/or health to regulations. The Health Effects Institute defines the air pollution accountability framework as a chain of events that includes the regulation of interest, air quality, exposure/dose, and health outcomes, and suggests that accountability research should address impacts for each of these linkages. Early accountability studies investigated short-term, local regulatory actions (for example, coal use banned city-wide on a specific date or traffic pattern changes made for Olympic Games). Recent studies assessed regulations implemented over longer time and larger spatial scales. Studies on broader scales require accountability research methods that account for effects of confounding factors that increase over time and space. Improved estimates of appropriate baseline levels (sometimes termed "counterfactual"-the expected state in a scenario without an intervention) that account for confounders and uncertainties at each link in the accountability chain will help estimate causality with greater certainty. In the direct accountability framework, researchers link outcomes with regulations using statistical methods that bypass the link-by-link approach of classical accountability. Direct accountability results and methods complement the classical approach. New studies should take advantage of advanced planning for accountability studies, new data sources (such as satellite measurements), and new statistical methods. Evaluation of new methods and data sources is necessary to improve investigations of long-term regulations, and associated uncertainty should be accounted for at each link to provide a confidence estimate of air quality regulation effectiveness. The final step in any accountability is the comparison of results with the proposed benefits of an air quality policy.

IMPLICATIONS

The field of air pollution accountability continues to grow in importance to a number of stakeholders. Two frameworks, the classical accountability chain and direct accountability, have been used to estimate impacts of regulatory actions, and both require careful attention to confounders and uncertainties. Researchers should continue to develop and evaluate both methods as they investigate current and future air pollution regulations.

Co-benefits of global and regional greenhouse gas mitigation on U.S. air quality in 2050

Policies to mitigate greenhouse gas (GHG) emissions will not only slow climate change, but can also have ancillary benefits of improved air quality. Here we examine the co-benefits of both global and regional GHG mitigation on U.S. air quality in 2050 at fine resolution, using dynamical downscaling methods, building on a previous global co-benefits study (West et al., 2013). The co-benefits for U.S. air quality are quantified via two mechanisms: through reductions in co-emitted air pollutants from the same sources, and by slowing climate change and its influence on air quality, following West et al. (2013). Additionally, we separate the total co-benefits into contributions from domestic GHG mitigation versus mitigation in foreign countries. We use the WRF model to dynamically downscale future global climate to the regional scale, the SMOKE program to directly process global anthropogenic emissions into the regional domain, and we provide dynamical boundary conditions from global simulations to the regional CMAQ model. The total co-benefits of global GHG mitigation from the RCP4.5 scenario compared with its reference are estimated to be higher in the eastern U.S. (ranging from 0.6-1.0 μg m-3) than the west (0-0.4 μg m-3) for PM2.5, with an average of 0.47 μg m-3 over U.S.; for O3, the total co-benefits are more uniform at 2-5 ppb with U.S. average of 3.55 ppb. Comparing the two mechanisms of co-benefits, we find that reductions of co-emitted air pollutants have a much greater influence on both PM2.5 (96% of the total co-benefits) and O3 (89% of the total) than the second co-benefits mechanism via slowing climate change, consistent with West et al. (2013). GHG mitigation from foreign countries contributes more to the U.S. O3 reduction (76% of the total) than that from domestic GHG mitigation only (24%), highlighting the importance of global methane reductions and the intercontinental transport of air pollutants. For PM2.5, the benefits of domestic GHG control are greater (74% of total). Since foreign contributions to co-benefits can be substantial, with foreign O3 benefits much larger than those from domestic reductions, previous studies that focus on local or regional co-benefits may greatly underestimate the total co-benefits of global GHG reductions. We conclude that the U.S. can gain significantly greater domestic air quality co-benefits by engaging with other nations to control GHGs.

Projecting Fine Particulate Matter-Related Mortality in East China

China is suffering from severe air pollution from fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM2.5)], especially East China. But its future trends and potential health impacts remain unclear. The study objectives were to project future trends of PM2.5 and its short-term effect on mortality in East China by 2030. First, daily changes in PM2.5 concentrations between 2005 and 2030 were projected under the "current legislation" scenario (CLE) and the "maximum technically feasible reduction" scenario (MFR). Then, they were linked to six population projections, two mortality rate projections, and PM2.5-mortality associations to estimate the changes in PM2.5-related mortality in East China between 2005 and 2030. Under the CLE scenario, the annual mean PM2.5 concentration was projected to decrease by 0.62 μg/m(3) in East China, which could cause up to 124,000 additional deaths, when considering the population growth. Under the MFR scenario, the annual mean PM2.5 concentration was projected to decrease by 20.41 μg/m(3) in East China. At least 230,000 deaths could be avoided by such a large reduction in PM2.5 concentration under MFR scenario, even after accounting for the population growth. Therefore, our results suggest that reducing PM2.5 concentration substantially in East China would benefit the public health. Otherwise, it may still remain as a great health risk in the future, especially when the population keeps growing.

Regional air quality management aspects of climate change: impact of climate mitigation options on regional air emissions

We investigate the projected impact of six climate mitigation scenarios on U.S. emissions of carbon dioxide (CO2), sulfur dioxide (SO2), and nitrogen oxides (NOX) associated with energy use in major sectors of the U.S. economy (commercial, residential, industrial, electricity generation, and transportation). We use the EPA U.S. 9-region national database with the MARKet Allocation energy system model to project emissions changes over the 2005 to 2050 time frame. The modeled scenarios are two carbon tax, two low carbon transportation, and two biomass fuel choice scenarios. In the lower carbon tax and both biomass fuel choice scenarios, SO2 and NOX achieve reductions largely through pre-existing rules and policies, with only relatively modest additional changes occurring from the climate mitigation measures. The higher carbon tax scenario projects greater declines in CO2 and SO2 relative to the 2050 reference case, but electricity sector NOX increases. This is a result of reduced investments in power plant NOX controls in earlier years in anticipation of accelerated coal power plant retirements, energy penalties associated with carbon capture systems, and shifting of NOX emissions in later years from power plants subject to a regional NOX cap to those in regions not subject to the cap.

Impacts of potential CO2-reduction policies on air quality in the United States

Impacts of emissions changes from four potential U.S. CO2 emission reduction policies on 2050 air quality are analyzed using the community multiscale air quality model (CMAQ). Future meteorology was downscaled from the Goddard Institute for Space Studies (GISS) ModelE General Circulation Model (GCM) to the regional scale using the Weather Research Forecasting (WRF) model. We use emissions growth factors from the EPAUS9r MARKAL model to project emissions inventories for two climate tax scenarios, a combined transportation and energy scenario, a biomass energy scenario and a reference case. Implementation of a relatively aggressive carbon tax leads to improved PM2.5 air quality compared to the reference case as incentives increase for facilities to install flue-gas desulfurization (FGD) and carbon capture and sequestration (CCS) technologies. However, less capital is available to install NOX reduction technologies, resulting in an O3 increase. A policy aimed at reducing CO2 from the transportation sector and electricity production sectors leads to reduced emissions of mobile source NOX, thus reducing O3. Over most of the U.S., this scenario leads to reduced PM2.5 concentrations. However, increased primary PM2.5 emissions associated with fuel switching in the residential and industrial sectors leads to increased organic matter (OM) and PM2.5 in some cities.

Projecting future air pollution-related mortality under a changing climate: progress, uncertainties and research needs

BACKGROUND

Climate change may affect mortality associated with air pollutants, especially for fine particulate matter (PM2.5) and ozone (O3). Projection studies of such kind involve complicated modelling approaches with uncertainties.

OBJECTIVES

We conducted a systematic review of researches and methods for projecting future PM2.5-/O3-related mortality to identify the uncertainties and optimal approaches for handling uncertainty.

METHODS

A literature search was conducted in October 2013, using the electronic databases: PubMed, Scopus, ScienceDirect, ProQuest, and Web of Science. The search was limited to peer-reviewed journal articles published in English from January 1980 to September 2013.

DISCUSSION

Fifteen studies fulfilled the inclusion criteria. Most studies reported that an increase of climate change-induced PM2.5 and O3 may result in an increase in mortality. However, little research has been conducted in developing countries with high emissions and dense populations. Additionally, health effects induced by PM2.5 may dominate compared to those caused by O3, but projection studies of PM2.5-related mortality are fewer than those of O3-related mortality. There is a considerable variation in approaches of scenario-based projection researches, which makes it difficult to compare results. Multiple scenarios, models and downscaling methods have been used to reduce uncertainties. However, few studies have discussed what the main source of uncertainties is and which uncertainty could be most effectively reduced.

CONCLUSIONS

Projecting air pollution-related mortality requires a systematic consideration of assumptions and uncertainties, which will significantly aid policymakers in efforts to manage potential impacts of PM2.5 and O3 on mortality in the context of climate change.

Spatially resolved estimation of ozone-related mortality in the United States under two Representative Concentration Pathways (RCPs) and their uncertainty

The spatial pattern of the uncertainty in air pollution-related health impacts due to climate change has rarely been studied due to the lack of high-resolution model simulations, especially under the Representative Concentration Pathways (RCPs), the latest greenhouse gas emission pathways. We estimated future tropospheric ozone (O3) and related excess mortality and evaluated the associated uncertainties in the continental United States under RCPs. Based on dynamically downscaled climate model simulations, we calculated changes in O3 level at 12 km resolution between the future (2057-2059) and base years (2001-2004) under a low-to-medium emission scenario (RCP4.5) and a fossil fuel intensive emission scenario (RCP8.5). We then estimated the excess mortality attributable to changes in O3. Finally, we analyzed the sensitivity of the excess mortality estimates to the input variables and the uncertainty in the excess mortality estimation using Monte Carlo simulations. O3-related premature deaths in the continental U.S. were estimated to be 1,312 deaths/year under RCP8.5 (95% confidence interval (CI): 427 to 2,198) and -2,118 deaths/year under RCP4.5 (95% CI: -3,021 to -1,216), when allowing for climate change and emissions reduction. The uncertainty of O3-related excess mortality estimates was mainly caused by RCP emissions pathways. Excess mortality estimates attributable to the combined effect of climate and emission changes on O3 as well as the associated uncertainties vary substantially in space and so do the most influential input variables. Spatially resolved data is crucial to develop effective community level mitigation and adaptation policy.

Impact of climate change on mercury concentrations and deposition in the eastern United States

The global-regional climate-air pollution modeling system (GRE-CAPS) was applied over the eastern United States to study the impact of climate change on the concentration and deposition of atmospheric mercury. Summer and winter periods (300 days for each) were simulated, and the present-day model predictions (2000s) were compared to the future ones (2050s) assuming constant emissions. Climate change affects Hg(2+) concentrations in both periods. On average, atmospheric Hg(2+) levels are predicted to increase in the future by 3% in summer and 5% in winter respectively due to enhanced oxidation of Hg(0) under higher temperatures. The predicted concentration change of Hg(2+) was found to vary significantly in space due to regional-scale changes in precipitation, ranging from -30% to 30% during summer and -20% to 40% during winter. Particulate mercury, Hg(p) has a similar spatial response to climate change as Hg(2+), while Hg(0) levels are not predicted to change significantly. In both periods, the response of mercury deposition to climate change varies spatially with an average predicted increase of 6% during summer and 4% during winter. During summer, deposition increases are predicted mostly in the western parts of the domain while mercury deposition is predicted to decrease in the Northeast and also in many areas in the Midwest and Southeast. During winter mercury deposition is predicted to change from -30% to 50% mainly due to the changes in rainfall and the corresponding changes in wet deposition.

Quantifying the health impacts of air pollution under a changing climate-a review of approaches and methodology

Climate change has been predicted to affect future air quality, with inevitable consequences for health. Quantifying the health effects of air pollution under a changing climate is crucial to provide evidence for actions to safeguard future populations. In this paper, we review published methods for quantifying health impacts to identify optimal approaches and ways in which existing challenges facing this line of research can be addressed. Most studies have employed a simplified methodology, while only a few have reported sensitivity analyses to assess sources of uncertainty. The limited investigations that do exist suggest that examining the health risk estimates should particularly take into account the uncertainty associated with future air pollution emissions scenarios, concentration-response functions, and future population growth and age structures. Knowledge gaps identified for future research include future health impacts from extreme air pollution events, interactions between temperature and air pollution effects on public health under a changing climate, and how population adaptation and behavioural changes in a warmer climate may modify exposure to air pollution and health consequences.

Evaluating the effects of climate change on summertime ozone using a relative response factor approach for policymakers

The impact of climate change on surface-level ozone is examined through a multiscale modeling effort that linked global and regional climate models to drive air quality model simulations. Results are quantified in terms of the relative response factor (RRF(E)), which estimates the relative change in peak ozone concentration for a given change in pollutant emissions (the subscript E is added to RRF to remind the reader that the RRF is due to emission changes only). A matrix of model simulations was conducted to examine the individual and combined effects offuture anthropogenic emissions, biogenic emissions, and climate on the RRF(E). For each member in the matrix of simulations the warmest and coolest summers were modeled for the present-day (1995-2004) and future (2045-2054) decades. A climate adjustment factor (CAF(C) or CAF(CB) when biogenic emissions are allowed to change with the future climate) was defined as the ratio of the average daily maximum 8-hr ozone simulated under a future climate to that simulated under the present-day climate, and a climate-adjusted RRF(EC) was calculated (RRF(EC) = RRF(E) x CAF(C)). In general, RRF(EC) > RRF(E), which suggests additional emission controls will be required to achieve the same reduction in ozone that would have been achieved in the absence of climate change. Changes in biogenic emissions generally have a smaller impact on the RRF(E) than does future climate change itself The direction of the biogenic effect appears closely linked to organic-nitrate chemistry and whether ozone formation is limited by volatile organic compounds (VOC) or oxides of nitrogen (NO(x) = NO + NO2). Regions that are generally NO(x) limited show a decrease in ozone and RRF(EC), while VOC-limited regions show an increase in ozone and RRF(EC). Comparing results to a previous study using different climate assumptions and models showed large variability in the CAF(CB).

IMPLICATIONS

We present a methodology for adjusting the RRF to account for the influence of climate change on ozone. The findings of this work suggest that in some geographic regions, climate change has the potential to negate decreases in surface ozone concentrations that would otherwise be achieved through ozone mitigation strategies. In regions of high biogenic VOC emissions relative to anthropogenic NO(x) emissions, the impact of climate change is somewhat reduced, while the opposite is true in regions of high anthropogenic NO(x) emissions relative to biogenic VOC emissions. Further, different future climate realizations are shown to impact ozone in different ways.

Global air quality and climate

Emissions of air pollutants and their precursors determine regional air quality and can alter climate. Climate change can perturb the long-range transport, chemical processing, and local meteorology that influence air pollution. We review the implications of projected changes in methane (CH(4)), ozone precursors (O(3)), and aerosols for climate (expressed in terms of the radiative forcing metric or changes in global surface temperature) and hemispheric-to-continental scale air quality. Reducing the O(3) precursor CH(4) would slow near-term warming by decreasing both CH(4) and tropospheric O(3). Uncertainty remains as to the net climate forcing from anthropogenic nitrogen oxide (NO(x)) emissions, which increase tropospheric O(3) (warming) but also increase aerosols and decrease CH(4) (both cooling). Anthropogenic emissions of carbon monoxide (CO) and non-CH(4) volatile organic compounds (NMVOC) warm by increasing both O(3) and CH(4). Radiative impacts from secondary organic aerosols (SOA) are poorly understood. Black carbon emission controls, by reducing the absorption of sunlight in the atmosphere and on snow and ice, have the potential to slow near-term warming, but uncertainties in coincident emissions of reflective (cooling) aerosols and poorly constrained cloud indirect effects confound robust estimates of net climate impacts. Reducing sulfate and nitrate aerosols would improve air quality and lessen interference with the hydrologic cycle, but lead to warming. A holistic and balanced view is thus needed to assess how air pollution controls influence climate; a first step towards this goal involves estimating net climate impacts from individual emission sectors. Modeling and observational analyses suggest a warming climate degrades air quality (increasing surface O(3) and particulate matter) in many populated regions, including during pollution episodes. Prior Intergovernmental Panel on Climate Change (IPCC) scenarios (SRES) allowed unconstrained growth, whereas the Representative Concentration Pathway (RCP) scenarios assume uniformly an aggressive reduction, of air pollutant emissions. New estimates from the current generation of chemistry-climate models with RCP emissions thus project improved air quality over the next century relative to those using the IPCC SRES scenarios. These two sets of projections likely bracket possible futures. We find that uncertainty in emission-driven changes in air quality is generally greater than uncertainty in climate-driven changes. Confidence in air quality projections is limited by the reliability of anthropogenic emission trajectories and the uncertainties in regional climate responses, feedbacks with the terrestrial biosphere, and oxidation pathways affecting O(3) and SOA.

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.

Sensitivity of air pollution-induced premature mortality to precursor emissions under the influence of climate change

The relative contributions of PM(2.5) and ozone precursor emissions to air pollution-related premature mortality modulated by climate change are estimated for the U.S. using sensitivities of air pollutants to precursor emissions and health outcomes for 2001 and 2050. Result suggests that states with high emission rates and significant premature mortality increases induced by PM(2.5) will substantially benefit in the future from SO(2), anthropogenic NO(X) and NH(3) emissions reductions while states with premature mortality increases induced by O(3) will benefit mainly from anthropogenic NO(X) emissions reduction. Much of the increase in premature mortality expected from climate change-induced pollutant increases can be offset by targeting a specific precursor emission in most states based on the modeling approach followed here.

Potential impact of climate change on air pollution-related human health effects

The potential health impact of ambient ozone and PM2.5 concentrations modulated by climate change over the United States is investigated using combined atmospheric and health modeling. Regional air quality modeling for 2001 and 2050 was conducted using CMAQ Modeling System with meteorology from the GISS Global Climate Model, downscaled regionally using MM5,keeping boundary conditions of air pollutants, emission sources, population, activity levels, and pollution controls constant. BenMap was employed to estimate the air pollution health outcomes at the county, state, and national level for 2050 caused by the effect of meteorology on future ozone and PM2.5 concentrations. The changes in calculated annual mean PM2.5 concentrations show a relatively modest change with positive and negative responses (increasing PM2.5 levels across the northeastern U.S.) although average ozone levels slightly decrease across the northern sections of the U.S., and increase across the southern tier. Results suggest that climate change driven air quality-related health effects will be adversely affected in more then 2/3 of the continental U.S. Changes in health effects induced by PM2.5 dominate compared to those caused by ozone. PM2.5-induced premature mortality is about 15 times higher then that due to ozone. Nationally the analysis suggests approximately 4000 additional annual premature deaths due to climate change impacts on PM2.5 vs 300 due to climate change-induced ozone changes. However, the impacts vary spatially. Increased premature mortality due to elevated ozone concentrations will be offset by lower mortality from reductions in PM2.5 in 11 states. Uncertainties related to different emissions projections used to simulate future climate, and the uncertainties forecasting the meteorology, are large although there are potentially important unaddressed uncertainties (e.g., downscaling, speciation, interaction, exposure, and concentration-response function of the human health studies).

Effect of climate change between 1984 and 2007 on precipitation chemistry at a site in northeastern U.S.A

Climate change predictions for the northeastern US call for an increase in tropical storms and a decrease in extra tropical cyclones including continental storms. We ran 24-h back trajectories for each precipitation event that occurred at the Cary Institute of Ecosystem Studies in southeastern New York, U.S.A. from 1984 to 2007 and analyzed precipitation chemistry as well as air mass position 24 h prior to the onset of each precipitation event. The results showed an increase in marine precipitation and a slight but statistically insignificant decrease in continental precipitation during the 1984-2007 period. The chemistry of precipitation from the two directions was quite different marine storms were higher in Na4 and Cl- but lower in solutes associated with acid precipitation (H+, SO4(2-), NO3-, and NH4+). Annual mean concentrations of acid precipitation solutes declined for storms from both directions during the period. We used a simple mixing model based on the current rates of increase and decrease of marine and continental precipitation respectively to show that chemical changes in precipitation resulting from the shift in storm tracks are small compared to chemical changes due to emissions reductions.

U.S. ozone air quality under changing climate and anthropogenic emissions

We examined future ozone (O3) air quality in the United States (U.S.) under changing climate and anthropogenic emissions worldwide by performing global climate-chemistry simulations, utilizing various combinations of present (1990s) and future (Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 2050s) climates, and present and future (2050s; IPCC SRES A2 and B1) anthropogenic emissions. The A2 climate scenario is employed here because it lies at the upper extreme of projected climate change for the 21st century. To examine the sensitivity of U.S. O3 to regional emissions increases (decreases), the IPCC SRES A2 and B1 scenarios, which have overall higher and lower O3-precursor emissions for the U.S., respectively, have been chosen. We find that climate change, by itself, significantly worsens the severity and frequency of high-O3 events ("episodes") over most locations in the U.S., with relatively small changes in average O3 air quality. These high-O3 increases due to climate change alone will erode moderately the gains made under a U.S. emissions reduction scenario (e.g., B1). The effect of climate change on high- and average-O3 increases with anthropogenic emissions. Insofar as average O3 air quality is concerned, changes in U.S. anthropogenic emissions will play the most important role in attaining (or not) near-term U.S. O3 air quality standards. However, policy makers must plan appropriately for O3 background increases due to projected increases in global CH4 abundance and non-U.S. anthropogenic emissions, as well as potential local enhancements that they could cause. These findings provide strong incentives for more-than-planned emissions reductions at locations that are currently O3-nonattainment.