Fine particulate matter damages and value added in the US economy

Impacts assessment of nitrification inhibitors on U.S. agricultural emissions of reactive nitrogen gases

Fertilizer-intensive agriculture leads to emissions of reactive nitrogen (Nr), posing threats to climate via nitrous oxide (N2O) and to air quality and human health via nitric oxide (NO) and ammonia (NH3) that form ozone and particulate matter (PM) downwind. Adding nitrification inhibitors (NIs) to fertilizers can mitigate N2O and NO emissions but may stimulate NH3 emissions. Quantifying the net effects of these trade-offs requires spatially resolving changes in emissions and associated impacts. We introduce an assessment framework to quantify such trade-off effects. It deploys an agroecosystem model with enhanced capabilities to predict emissions of Nr with or without the use of NIs, and a social cost of greenhouse gas to monetize the impacts of N2O on climate. The framework also incorporates reduced-complexity air quality and health models to monetize associated impacts of NO and NH3 emissions on human health downwind via ozone and PM. Evaluation of our model against available field measurements showed that it captured the direction of emission changes but underestimated reductions in N2O and overestimated increases in NH3 emissions. The model estimated that, averaged over applicable U.S. agricultural soils, NIs could reduce N2O and NO emissions by an average of 11% and 16%, respectively, while stimulating NH3 emissions by 87%. Impacts are largest in regions with moderate soil temperatures and occur mostly within two to three months of N fertilizer and NI application. An alternative estimate of NI-induced emission changes was obtained by multiplying the baseline emissions from the agroecosystem model by the reported relative changes in Nr emissions suggested from a global meta-analysis: -44% for N2O, -24% for NO and +20% for NH3. Monetized assessments indicate that on an annual scale, NI-induced harms from increased NH3 emissions outweigh (8.5-33.8 times) the benefits of reducing NO and N2O emissions in all agricultural regions, according to model-based estimates. Even under meta-analysis-based estimates, NI-induced damages exceed benefits by a factor of 1.1-4. Our study highlights the importance of considering multiple pollutants when assessing NIs, and underscores the need to mitigate NH3 emissions. Further field studies are needed to evaluate the robustness of multi-pollutant assessments.

Effect of aerobic exercise and particulate matter exposure duration on the diversity of gut microbiota

Inhalation of ambient particulate matter (PM) can disrupt the gut microbiome, while exercise independently influences the gut microbiome by promoting beneficial bacteria. In this study, we analyzed changes in gut microbial diversity and composition in response to combined interventions of PM exposure and aerobic exercise, extending up to 12 weeks. This investigation was conducted using mice, categorized into five groups: control group (Con), exercise group (EXE), exercise group followed by 3-day exposure to PM (EXE + 3-day PM), particulate matter exposure (PM), and PM exposure with concurrent treadmill exercise (PME). Notably, the PM group exhibited markedly lower alpha diversity and richness compared to the Con group and our analysis of beta diversity revealed significant variations among the intervention groups. Members of the Lachnospiraceae family showed significant enhancement in the exercise intervention groups (EXE and PME) compared to the Con and PM groups. The biomarker Lactobacillus, Coriobacteraceae, and Anaerofustis were enriched in the EXE group, while Desulfovibrionaceae, Mucispirillum schaedleri, Lactococcus and Anaeroplasma were highly enriched in the PM group. Differential abundance analysis revealed that Paraprevotella, Bacteroides, and Blautia were less abundant in the 12-week PM exposure group than in the 3-day PM exposure group. Moreover, both the 3-day and 12-week PM exposure groups exhibited a reduced relative abundance of Bacteroides uniformis, SMB53, and Staphylococcus compared to non-PM exposure groups. These findings will help delineate the possible roles and associations of altered microbiota resulting from the studied interventions, paving the way for future mechanistic research.

Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado

We evaluated the sensitivity of estimated PM2.5 and NO2 health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration-response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO2, but not PM2.5, attributable deaths. Using county-level predictions instead of 1 km2 predictions of NO2 resulted in a lower estimate of mortality attributable to NO2 by ∼ 50 % for all of Colorado for each year between 2000 and 2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a 130 % higher estimate of annual mortality attributable for the white population and a 40 % and 80 % lower estimate of mortality attributable to PM2.5 for Black and Hispanic residents, respectively. Using racial/ethnic group specific CRFs did not result in a different estimation of NO2 attributable mortality for white residents, but led to ∼ 50 % lower estimates of mortality for Black residents, and 290 % lower estimate for Hispanic residents. Using NO2 based on home instead of home and workplace locations results in a smaller estimate of annual mortality attributable to NO2 for all of Colorado by 2 % each year and 0.3 % for PM2.5. Our results should be interpreted as an exercise to make methodological recommendations for future health impact assessments of pollution.

Anti-Inflammatory Artificial Extracellular Vesicles with Notable Inhibition of Particulate Matter-Induced Skin Inflammation and Barrier Function Impairment

Particulate matter (PM) exposure disrupts the skin barrier, causing cutaneous inflammation that may eventually contribute to the development of various skin diseases. Herein, we introduce anti-inflammatory artificial extracellular vesicles (AEVs) fabricated through cell extrusion using the biosurfactant PEGylated mannosylerythritol lipid (P-MEL), hereafter named AEVP-MEL. The P-MEL has anti-inflammatory abilities with demonstrated efficacy in inhibiting the secretion of pro-inflammatory mediators. Mechanistically, AEVP-MEL enhanced anti-inflammatory response by inhibiting the mitogen-activated protein kinase (MAPK) pathway and decreasing the release of inflammatory mediators such as reactive oxygen species (ROS), cyclooxygenase-2 (COX-2), and pro-inflammatory cytokines in human keratinocytes. Moreover, AEVP-MEL promoted increased expression levels of skin barrier proteins (e.g., involucrin, IVL) and water-proteins (e.g., aquaporin 3, AQP3). In vivo studies revealed that repeated PM exposure to intact skin resulted in cutaneous inflammatory responses, including increased skin thickness (hyperkeratosis) and mast cell infiltration. Importantly, our data showed that the AEVP-MEL treatment significantly restored immune homeostasis in the skin affected by PM-induced inflammation and enhanced the intrinsic skin barrier function. This study highlights the potential of the AEVP-MEL in promoting skin health against PM exposure and its promising implications for the prevention and treatment of PM-related skin disorders.

Exploring Regional Reduction Pathways for Human Exposure to Fine Particulate Matter (PM2.5) Using a Traffic Assignment Model

An exposure-based traffic assignment (TA) model is used to quantify primary and secondary fine particulate matter (PM2.5) exposure from on-road vehicle flow on the Chicago Metropolitan Area regional network. PM2.5 exposure due to emissions from light-duty vehicles, heavy-duty trucks, public transportation, and electricity generation for electric vehicle charging and light-rail transportation is considered. The model uses travel demand data disaggregated by time-of-day period and vehicle user class to compare the exposure impacts of two TA optimization scenarios: a baseline user equilibrium with respect to travel time (UET) and a system optimal with respect to pollutant intake (SOI). Estimated baseline PM2.5 exposure damages are $3.7B-$8.3B/year. The SOI uses exposure-based vehicle rerouting to reduce total damages by 8.2%, with high-impacted populations benefiting from 10% to 20% reductions. However, the SOI's rerouting principle leads to a 66% increase in travel time. The model is then used to quantify the mitigation potential of different exposure reduction strategies, including a bi-objective optimization formulation that minimizes travel time and PM2.5 exposure concurrently, adoption of a cleaner vehicle fleet, higher public transportation use, particle filtration, and exposure-based truck routing. Exposure reductions range between 1% and 40%, but collective adoption of all strategies would lead to reductions upward of 50%.

The contribution of wildfire to PM2.5 trends in the USA

Steady improvements in ambient air quality in the USA over the past several decades, in part a result of public policy1,2, have led to public health benefits1-4. However, recent trends in ambient concentrations of particulate matter with diameters less than 2.5 μm (PM2.5), a pollutant regulated under the Clean Air Act1, have stagnated or begun to reverse throughout much of the USA5. Here we use a combination of ground- and satellite-based air pollution data from 2000 to 2022 to quantify the contribution of wildfire smoke to these PM2.5 trends. We find that since at least 2016, wildfire smoke has influenced trends in average annual PM2.5 concentrations in nearly three-quarters of states in the contiguous USA, eroding about 25% of previous multi-decadal progress in reducing PM2.5 concentrations on average in those states, equivalent to 4 years of air quality progress, and more than 50% in many western states. Smoke influence on trends in the number of days with extreme PM2.5 concentrations is detectable by 2011, but the influence can be detected primarily in western and mid-western states. Wildfire-driven increases in ambient PM2.5 concentrations are unregulated under current air pollution law6 and, in the absence of further interventions, we show that the contribution of wildfire to regional and national air quality trends is likely to grow as the climate continues to warm.

Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition

Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. Simulating pollution concentration is a crucial step of the estimation, but traditional approaches often rely on complicated chemical transport models that require extensive expertise and computational resources. In this study, we develop a machine learning framework that is able to provide precise and robust annual average fine particle (PM2.5) concentration estimations directly from a high-resolution fossil energy use dataset. Applications of the framework with Chinese data reveal highly heterogeneous health benefits of avoiding premature mortality by reducing fossil fuel use in different sectors and regions in China with a mean of $19/tCO2 and a standard deviation of $38/tCO2. Reducing rural and residential coal use offers the highest co-benefits with a mean of $151/tCO2. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition toward a carbon-neutral energy system.

Polluting under the Radar: Emissions, Inequality, and Concrete Batch Plants in Houston

Small industrial sources collectively release large amounts of pollution, including particulate matter (PM) that contributes to air quality problems in the United States and elsewhere. We study one such type of industrial facility, concrete batch plants, and analyze PM emissions and siting patterns of 131 plants located in Harris County, Texas. We find that concrete batch plants in Harris County are collectively a major pollution source, contributing between 38 and 111 tons of primary PM2.5 emissions (between 26%-76% of PM2.5 from the median Texas oil refinery) and between 109 and 493 tons of primary PM10 emissions (between 64%-290% of PM10 from the median refinery). Estimates from an integrated assessment model suggest that health damages from the PM2.5 emissions alone amount to $29 million annually, reflecting two additional premature deaths per year. We further find that concrete batch plants in Harris County are disproportionately located in census tracts with more low-income, Hispanic, and Black populations, thereby raising important environmental justice questions. On the basis of these findings, we argue that small pollution sources require more air quality monitoring and emissions reporting and that regulatory agencies should consider cumulative environmental and health impacts of these sources as part of the permitting process.

The local impacts of coal and oil power plant retirements on air pollution and cardiorespiratory health in California: An application of generalized synthetic control method

BACKGROUND

This study capitalized on coal and oil facility retirements to quantify their potential effects on fine particulate matter (PM2.5) concentrations and cardiorespiratory hospitalizations in affected areas using a generalized synthetic control method.

METHODS

We identified 11 coal and oil facilities in California that retired between 2006 and 2013. We classified zip code tabulation areas (ZCTA) as exposed or unexposed to a facility retirement using emissions information, distance, and a dispersion model. We calculated weekly ZCTA-specific PM2.5 concentrations based on previously estimated daily time-series PM2.5 concentrations from an ensemble model, and weekly cardiorespiratory hospitalization rates based on hospitalization data collected by the California Department of Health Care Access and Information. We estimated the average differences in weekly average PM2.5 concentrations and cardiorespiratory hospitalization rates in four weeks after each facility retirement between the exposed ZCTAs and the synthetic control using all unexposed ZCTAs (i.e., the average treatment effect among the treated [ATT]) and pooled ATTs using meta-analysis. We conducted sensitivity analyses to consider different classification schemes to distinguish exposed from unexposed ZCTAs, including aggregating outcomes with different time intervals and including a subset of facilities with reported retirement date confirmed via emission record.

RESULTS

The pooled ATTs were 0.02 μg/m3 (95% confidence interval (CI): -0.25 to 0.29 μg/m3) and 0.34 per 10,000 person-weeks (95%CI: -0.08 to 0.75 per 10,000 person-weeks) following the facility closure for weekly PM2.5 and cardiorespiratory hospitalization rates, respectively. Our inferences remained the same after conducting sensitivity analyses.

CONCLUSIONS

We demonstrated a novel approach to study the potential benefits associated with industrial facility retirements. The declining contribution of industrial emissions to ambient air pollution in California may explain our null findings. We encourage future research to replicate this work in regions with different industrial activities.

Exploring Regional Fine Particulate Matter (PM2.5) Exposure Reduction Pathways Using an Optimal Power Flow Model: The Case of the Illinois Power Grid

This work develops an exposure-based optimal power flow model (OPF) that accounts for fine particulate matter (PM_2.5) exposure from electricity generation unit (EGU) emissions. Advancing health-based dispatch models to an OPF with transmission constraints and reactive power flow is an essential development given its utility for short- and long-term planning by system operators. The model enables the assessment of the exposure mitigation potential and the feasibility of intervention strategies while still prioritizing system costs and network stability. A representation of the Illinois power grid is developed to demonstrate how the model can inform decision making. Three scenarios minimizing dispatch costs and/or exposure damages are simulated. Other interventions assessed include adopting best-available EGU emission control technologies, having higher renewable generation, and relocating high-polluting EGUs. Neglecting transmission constraints fails to account for 4% of exposure damages ($60 M/y) and dispatch costs ($240 M/y). Accounting for exposure in the OPF reduces damages by 70%, a reduction on the order of that achieved by high renewable integration. About 80% of all exposure is attributed to EGUs fulfilling only 25% of electricity demand. Siting these EGUs in low-exposure zones avoids 43% of all exposure. Operation and cost advantages inherent to each strategy beyond exposure reduction suggest their collective adoption for maximum benefits.

A low-carbon electricity sector in Europe risks sustaining regional inequalities in benefits and vulnerabilities

Improving equity is an emerging priority in climate and energy strategies, but little is known how these strategies would alter inequalities. Regional inequalities such as price, employment and land use are especially relevant in the electricity sector, which must decarbonize first to allow other sectors to decarbonize. Here, we show that a European low-carbon electricity sector in 2035 can reduce but also sustain associated regional inequalities. Using spatially-explicit modeling for 296 sub-national regions, we demonstrate that emission cuts consistent with net-zero greenhouse gas emissions in 2050 result in continent-wide benefits by 2035 regarding electricity sector investments, employment gains, and decreased greenhouse gas and particulate matter emissions. However, the benefits risk being concentrated in affluent regions of Northern Europe, while regions of Southern and Southeastern Europe risk high vulnerabilities due to high adverse impacts and sensitivities, and low adaptive capacities. Future analysis should investigate policy mechanisms for reducing and compensating inequalities.

Total Social Costs and Benefits of Long-Distance Hydropower Transmission

Increasing amounts of hydropower are being exported from Canada to the northern United States. Recently proposed projects would increase transmission capacity to U.S. population centers without increasing generation. This avoids generation-side impacts from hydroelectric development and introduces power to the U.S. energy mix that is dispatchable, unlike wind and solar, with greenhouse gas emissions generally lower than those of fossil fuels. There is, however, a lack of analysis comparing high upfront capital costs to social benefits and controversy over valuation of social costs of hydropower from existing generation given the negligible marginal cost of production. This analysis evaluates direct and indirect costs in comparison to alternatives for a 1250 MW transmission line from Canada to New York City currently under development to replace the recent loss of ∼15 TWh year^-1 of nuclear generation. For the case study considered, we find that long-distance transmission avoids $13.2 billion ($12.1-14.4 billion) in total social costs by 2050. This includes $4.2 billion ($3.4-5.1 billion) from premature mortality in disproportionately Hispanic and African American or Black counties (roughly 306 avoided deaths). In an extensive sensitivity analysis, results are robust to all modeling choices other than the cost assigned to hydropower: the nominal dollar value of hydropower imports (payments from buyer to seller) commonly used in cost-benefit analysis leads to substantial underestimates of net benefits from transmission projects. The opportunity cost of these imports (e.g., environmental benefits foregone in alternative export markets) is a better metric for cost but is difficult to estimate.

Ammonia emissions from agriculture and their contribution to fine particulate matter: A review of implications for human health

Atmospheric ammonia (NH₃) released from agriculture is contributing significantly to acidification and atmospheric NH₃ may have on human health is much less readily available. The potential direct impact of NH₃ on the health of the general public is under-represented in scientific literature, though there have been several studies which indicate that NH₃ has a direct effect on the respiratory health of those who handle livestock. These health impacts can include a reduced lung function, irritation to the throat and eyes, and increased coughing and phlegm expulsion. More recent studies have indicated that agricultural NH₃ may directly influence the early on-set of asthma in young children. In addition to the potential direct impact of ammonia, it is also a substantial contributor to the fine particulate matter (PM_2.5) fraction (namely the US and Europe); where it accounts for the formation of 30% and 50% of all PM_2.5 respectively. PM_2.5 has the ability to penetrate deep into the lungs and cause long term illnesses such as Chronic Obstructive Pulmonary Disease (COPD) and lung cancer. Hence, PM_2.5 causes economic losses which equate to billions of dollars (US) to the global economy annually. Both premature deaths associated with the health impacts from PM_2.5 and economic losses could be mitigated with a reduction in NH₃ emissions resulting from agriculture. As agriculture contributes to more than 81% of all global NH₃ emissions, it is imperative that food production does not come at a cost to the world's ability to breathe; where reductions in NH₃ emissions can be easier to achieve than other associated pollutants.

Sources of ambient PM2.5 exposure in 96 global cities

To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM_2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside its boundaries. We use the Intervention Model for Air Pollution (InMAP) global-through-urban reduced complexity air quality model with a high-resolution, global inventory of pollutant emissions to quantify the contribution of emissions by source type and location for 96 global cities. Among these cities, we find that the fraction of PM_2.5 exposure caused by within-city emissions varies widely (μ = 37%; σ = 22%) and is not well-explained by surrounding population density. The list of most-important sources also varies by city. Compared to a more mechanistically detailed model, InMAP predicts urban measured concentrations with lower bias and error but also lower correlation. Predictive accuracy in urban areas is not particularly high with either model, suggesting an opportunity for improving global urban air emission inventories. We expect the results herein can be useful as a screening tool for policy options and, in the absence of available resources for further analysis, to inform policy action to improve public health.

Geographically resolved social cost of anthropogenic emissions accounting for both direct and climate-mediated effects

The magnitude and distribution of physical and societal impacts from long-lived greenhouse gases are insensitive to the emission source location; the same is not true for major coemitted short-lived pollutants such as aerosols. Here, we combine novel global climate model simulations with established response functions to show that a given aerosol emission from different regions produces divergent air quality and climate changes and associated human system impacts, both locally and globally. The marginal global damages to infant mortality, crop productivity, and economic growth from aerosol emissions and their climate effects differ by more than an order of magnitude depending on source region, with certain regions creating global external climate changes and impacts much larger than those felt locally. The complex distributions of aerosol-driven societal impacts emerge from geographically distinct and region-specific aerosol-climate interactions, estimation of which is enabled by the full Earth System Modeling Framework used here.

Integrated Modeling of U.S. Agricultural Soil Emissions of Reactive Nitrogen and Associated Impacts on Air Pollution, Health, and Climate

Agricultural soils are leading sources of reactive nitrogen (Nr) species including nitrogen oxides (NO_x), ammonia (NH₃), and nitrous oxide (N₂O). The propensity of NO_x and NH₃ to generate ozone and fine particulate matter and associated impacts on health are highly variable, whereas the climate impacts of long-lived N₂O are independent of emission timing and location. However, these impacts have rarely been compared on a spatially resolved monetized basis. In this study, we update the nitrogen scheme in an agroecosystem model to simulate the Nr emissions from fertilized soils across the contiguous United States. We then apply a reduced-form air pollution health effect model to assess air quality impacts from NO_x and NH₃ and a social cost of N₂O to assess the climate impacts. Assuming an $8.2 million value of a statistical life and a $13,100/ton social cost of N₂O, the air quality impacts are a factor of ∼7 to 15 times as large as the climate impacts in heavily populated coastal regions, whereas the ratios are closer to 2.5 in sparsely populated regions. Our results show that air pollution, health, and climate should be considered jointly in future assessments of how farming practices affect Nr emissions.

Mortality Attributable to Long-Term Exposure to Ambient Fine Particulate Matter: Insights from the Epidemiologic Evidence for Understudied Locations

Epidemiologic cohort studies have consistently demonstrated that long-term exposure to ambient fine particles (PM_2.5) is associated with mortality. Nevertheless, extrapolating results to understudied locations may involve considerable uncertainty. To explore this issue, this review discusses the evidence for (i) the associated risk of mortality, (ii) the shape of the concentration-response function, (iii) a causal interpretation, and (iv) how the source mix/composition of PM_2.5 and population characteristics may alter the effect. The accumulated evidence suggests the following: (i) In the United States, the change in all-cause mortality risk per μg/m³ is about 0.8%. (ii) The concentration-response function appears nonlinear. (iii) Causation is overwhelmingly supported. (iv) Fossil fuel combustion-related sources are likely more toxic than others, and age, race, and income may modify the effect. To illustrate the use of our findings in support of a risk assessment in an understudied setting, we consider Kuwait. However, given the complexity of this relationship and the heterogeneity in reported effects, it is unreasonable to think that, in such circumstances, point estimates can be meaningful. Consequently, quantitative probabilistic estimates, which cannot be derived objectively, become essential. Formally elicited expert judgment can provide such estimates, and this review provides the evidence to support an elicitation.

Cows as canaries: The effects of ambient air pollution exposure on milk production and somatic cell count in dairy cows

Exposure to air pollution, including criteria pollutants such as fine particulate matter (PM_2.5) and ozone (O₃), has been associated with morbidity and mortality in mammals. As a genetically homogenous population that is closely monitored for health, dairy cattle present a unique opportunity to assess the association between changes in air pollution and mammalian health. Milk yield decreases in the summer if temperature and humidity, measured by the Temperature Humidity Index (THI). As O₃ levels increase with warmer temperatures, and summer PM_2.5 may increase with wildfire smoke, dairy cows may serve as a useful sentinel species to evaluate subacute markers of inflammation and metabolic output and ambient pollution. Over two years, we assessed summertime O₃ and PM_2.5 concentrations from local US EPA air quality monitors into an auto-regressive mixed model of the association between THI and daily milk production data and bulk tank somatic cell count (SCC). In unadjusted models, a 10 unit increase THI was associated with 28,700 cells/mL (95% CI: 17,700, 39,690) increase in SCC. After controlling for ambient air pollutants, THI was associated with a 14,500 SCC increase (95% CI: 3,400, 25,680), a 48% decrease in effect compared to the crude model. Further, in fully adjusted models, PM_2.5 was associated with a 105,500 cells/mL (95% CI: 90,030, 121,050) increase in SCC. Similar results were found for milk production. Results were amplified when high PM_2.5 days (95th percentile of observed values) associated with wildfire smoke were removed from the analyses. Our results support the hypothesis that PM_2.5 confounds the relationships between THI and milk yield and somatic cell count. The results of this study can be used to inform strategies for intervention to mitigate these impacts at the dairy level and potentially contribute to a model where production animals can act as air quality sentinels.

Do temporal trends of associations between short-term exposure to fine particulate matter (PM2.5) and risk of hospitalizations differ by sub-populations and urbanicity-a study of 968 U.S. counties and the Medicare population

While associations between short-term exposure to fine particulate matter (PM_2.5) and risk of hospitalization are well documented and evidence suggests that such associations change over time, it is unclear whether these temporal changes exist in understudied less-urban areas or differ by sub-population. We analyzed daily time-series data of 968 continental U.S. counties for 2000-2016, with cause-specific hospitalization from Medicare claims and population-weighted PM_2.5 concentrations originally estimated at 1km × 1 km from a hybrid model. Circulatory and respiratory hospitalizations were categorized based on primary diagnosis codes at discharge. Using modified Bayesian hierarchical modelling, we evaluated the temporal trend in association between PM_2.5 and hospitalizations and whether disparities in this trend exist across individual-level characteristics (e.g., sex, age, race, and Medicaid eligibility as a proxy for socio-economic status) and urbanicity. Urbanicity was categorized into three levels by county-specific percentage of urban population based on urban rural delineation from the U.S. Census. In this cohort with understudied less-urban areas without regulatory monitors, we still found positive association between circulatory and respiratory hospitalization and short-term exposure to PM_2.5, with higher effect estimates towards the end of study period. Consistent with current literature, we identified significant disparity in associations by race, socioeconomic status and urbanicity. We found that the percentage change in circulatory hospitalization rate per 10 μg/m³ increase in PM_2.5 was higher in the 2008-2016 time period compared to the 2000-2007 period by 0.33% (95% posterior credible interval 0.22, 0.44%), 0.52% (0.33, 0.69%), and 0.67% (0.53, 0.83%) for low, medium and high tertiles of urban areas, respectively. We also observed significant differences in temporal trends of associations across socioeconomic status, sex, and age, indicating a possible widening in disparity of PM_2.5-related health burden. This study raises the importance of considering environmental justice issues in PM_2.5-related health impacts with respect to how associations may change over time.

Valuing Ecosystem Services for Agricultural TFP: A Review of Best Practices, Challenges, and Recommendations

This paper provides a brief overview of methods to incorporate ecosystem service values into measures of agricultural total factor productivity (TFP), both in theory and in practice. This includes a review of the academic literature, a summary of related economic index theory, and a comparison of agency guidelines. We consider areas of consensus between the agencies and the research literature, as well as open debates surrounding the implementation of a standardized ecosystem accounting framework to integrate with existing TFP measures. This helps to bridge the gap between theoretical approaches to measurement and valuation in the research literature and their implementation in practice by national accounting agencies. Better connecting theory to practice also serves to highlight common challenges in the field, including questions of definition, scope, and scale for ecosystem services, as well as data collection and dissemination. We end with a summary of recommendations for moving forward.

Exposure to concentrated ambient PM2.5 (CAPM) induces intestinal disturbance via inflammation and alternation of gut microbiome

Air pollution causes a great disease burden worldwide. Recent evidences suggested that PM_2.5 contributes to intestinal disease. The objective of present study was to investigate the influence of ambient PM_2.5 on intestinal tissue and microbiome via whole-body inhalation exposure. The results showed that high levels and prolonged periods exposure to concentrated ambient PM_2.5 (CAPM) could destroy the mucous layer of the colon, and significantly alter the mRNA expression of tight junction (Occludin and ZO-1) and inflammation-related (IL-6, IL-10 and IL-1β) genes in the colon, comparing with exposure to the filtered air (FA). The composition of intestinal microbiome at the phylum and genus levels also varied along with the exposure time and PM_2.5 levels. At the phylum level, Bacteroidetes was greatly decreased, while Proteobacteria was increased after exposure to CAPM, comparing with exposure to FA. At the genus level, Clostridium XlVa, Akkermansia and Acetatifactor, were significantly elevated exposure to CAPM, comparing with exposure to FA. Our results also indicated that high levels and prolonged periods exposure to CAPM altered metabolic functional pathways. The correlation analysis showed that the intestinal inflammation was related to the alteration of gut microbiome induced by CAPM exposure, which may be a potential mechanism that elucidates PM_2.5-induced intestinal diseases. These results extend our knowledge on the toxicology and health effects of ambient PM_2.5.

Mortality Risk from PM2.5: A Comparison of Modeling Approaches to Identify Disparities across Racial/Ethnic Groups in Policy Outcomes

BACKGROUND

Regulatory analyses of air pollution policies require the use of concentration-response functions and underlying health data to estimate the mortality and morbidity effects, as well as the resulting benefits, associated with policy-related changes in fine particulate matter ≤2.5μm (PM2.5)]. Common practice by U.S. federal agencies involves using underlying health data and concentration-response functions that are not differentiated by racial/ethnic group.

OBJECTIVES

We aim to explore the policy implications of using race/ethnicity-specific concentration-response functions and mortality data in comparison to standard approaches when estimating the impact of air pollution on non-White racial/ethnic subgroups.

METHODS

Using new estimates from the epidemiological literature on race/ethnicity-specific concentration-response functions paired with race/ethnicity-specific mortality rates, we estimated the mortality impacts of air pollution from all sources from a uniform increase in concentrations and from the regulations imposed by the Mercury Air Toxics Standards.

RESULTS

Use of race/ethnicity-specific information increased PM2.5-related premature mortality estimates in older populations by 9% and among older Black Americans by 150% for all-source pollution exposure. Under a uniform degradation of air quality and race/ethnicity-specific information, older Black Americans were found to have approximately 3 times higher mortality relative to White Americans, which is obscured under a non-race/ethnicity-specific modeling approach. Standard approaches of using non-racial/ethnic specific information underestimate the benefits of the Mercury Air Toxics Standards to older Black Americans by almost 60% and overestimate the benefits to older White Americans by 14% relative to using a race/ethnicity-specific modeling approach.

DISCUSSION

Policy analyses incorporating race/ethnicity-specific concentration-response functions and mortality data relative to nondifferentiated inputs underestimate the overall magnitude of PM2.5 mortality burden and the disparity in impacts on older Black American populations. Based on our results, we recommend that the best available race/ethnicity-specific inputs are used in regulatory assessments to understand and reduce environmental injustices. https://doi.org/10.1289/EHP9001.

How Does Air Pollution Influence Housing Prices in the Bay Area?

In this paper we examine the effects of localized air pollution measurements on the housing prices in Oakland, CA. With high-resolution air pollution measurements for NO, NO₂, and BC, we can assess the ambient air quality on a parcel-by-parcel basis within the study domain. We combine a spatial lag model with an instrumental variable method to consider both the spatial autocorrelation and endogeneity effects between housing prices and air pollution concentrations. To the best of our knowledge, this is the first work in this field that combines both spatial autocorrelation and endogeneity effects in one model with accurate air pollution concentration measurements for each individual parcel. We found a positive spatial autocorrelation with housing prices using Moral’s I (value of 0.276) with the total sample number of 26,386. Somewhat surprisingly, we found a positive relationship between air pollution and housing prices. There are several possible explanations for this finding. Homeowners in high demand, low-stock housing areas, such as our study, may be insensitive to air pollution when the overall ambient air quality is relatively good. It is also possible that under clean air conditions, low variability in pollutant concentrations has little effect on property values. These hypotheses could be verified with more high-resolution air pollution measurements with a diversity of regions.

Emissions and Health Implications of Pennsylvania's Entry into the Regional Greenhouse Gas Initiative

The Regional Greenhouse Gas Initiative (RGGI) is a cap-and-trade system targeting CO₂ emissions from the electricity sector in the northeastern United States. As a major power producer and carbon emitter, Pennsylvania plans to join RGGI in 2022, which will affect both the carbon market (i.e., RGGI) and the regional electricity market (i.e., PJM). Combining a PJM power system model with a reduced-form model of CO₂ emissions abatement from RGGI states that are not in PJM, we find the annual average emissions from power plants in Pennsylvania can be reduced by 40%, 79%, 68%, and 76% for CO₂, SO₂, NO_x, and PM_2.5, respectively, during 2022-2030. Then, based on a range of source-specific marginal damage estimates, we find the cumulative monetized health cobenefits to be 17.7 to 40.8 billion USD. However, the reduced emissions and health damages in Pennsylvania are slightly offset by increases in the other states in PJM that do not participate in RGGI. Our study hence highlights the potential cross-state leakage issue that warrants careful consideration in the policy design and implementation process.

The climate and health benefits from intensive building energy efficiency improvements

Intensive building energy efficiency improvements can reduce emissions from energy use, improving outdoor air quality and human health, but may also affect ventilation and indoor air quality. This study examines the effects of highly ambitious, yet feasible, building energy efficiency upgrades in the United States. Our energy efficiency scenarios, derived from the literature, lead to a 6 to 11% reduction in carbon dioxide emissions and 18 to 25% reductions in particulate matter (PM2.5) emissions in 2050. These reductions are complementary with a carbon pricing policy on electricity. However, our results also point to the importance of mitigating indoor PM2.5 emissions, improving PM2.5 filtration, and evaluating ventilation-related policies. Even with no further ventilation improvements, we estimate that intensive energy efficiency scenarios could prevent 1800 to 3600 premature deaths per year across the United States in 2050. With further investments in indoor air quality, this can rise to 2900 to 5100.

Expected Health Effects of Reduced Air Pollution from COVID-19 Social Distancing

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about 6% on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and, therefore, expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame.

Relationship Between the COVID-19 Pandemic and Ecological, Economic, and Social Conditions

The COVID-19 pandemic had huge impacts on the global world, with both a negative impact on society and economy but a positive one on nature. But this universal effect resulted in different infection rates from country to country. We analyzed the relationship between the pandemic and ecological, economic, and social conditions. All of these data were collected in 140 countries at six time points. Correlations were studied using univariate and multivariate regression models. The world was interpreted as a single global ecosystem consisting of ecosystem units representing countries. We first studied 140 countries around the world together, and infection rates were related to per capita GDP, Ecological Footprint, median age, urban population, and Biological Capacity, globally. We then ranked the 140 countries according to infection rates. We created four groups with 35 countries each. In the first group of countries, the infection rate was very high and correlated with the Ecological Footprint (consumption) and GDP per capita (production). This group is dominated by developed countries, and their ecological conditions have proved to be particularly significant. In country groups 2, 3, and 4, infection rates were high, medium, and low, respectively, and were mainly related to median age and urban population. In the scientific discussion, we have interpreted why infection rates are very high in developed countries. Sustainable ecosystems are balanced, unlike the ecosystems of developed countries. The resilience and the health of both natural ecosystems and humans are closely linked to the world of microbial communities, the microbiomes of the biosphere. It is clear that both the economy and society need to be in harmony with nature, creating sustainable ecosystems in developed countries as well.

Public perception of air pollution sources across Europe

Air pollution is one of the primary concerns of our society for its effect on human health and the environment. Among the policy measures that can be put in place to limit air pollutant emissions, end-of-pipe technologies and/or regulatory instruments may be implemented through legislative acts. Also, equally important are behavioural measures, requiring citizens' active involvement. The success of any measure to limit pollutant emissions requires the acceptance by the citizens that, in turn, implies a correct perception of the main pollutant emission drivers. We present here the comparison between the public perception of air pollution sources and the real-world situation through a survey carried out in seven European countries and involving 16 101 respondents. Our study shows a dramatic underestimation of the contribution of the agri-food sector to air pollution. This result is common to all respondents in the seven countries examined and only to a small extent depends on gender, age and socio-economic status of the respondents.

Air quality-related health damages of food

Agriculture is a major contributor to air pollution, the largest environmental risk factor for mortality in the United States and worldwide. It is largely unknown, however, how individual foods or entire diets affect human health via poor air quality. We show how food production negatively impacts human health by increasing atmospheric fine particulate matter (PM2.5), and we identify ways to reduce these negative impacts of agriculture. We quantify the air quality-related health damages attributable to 95 agricultural commodities and 67 final food products, which encompass >99% of agricultural production in the United States. Agricultural production in the United States results in 17,900 annual air quality-related deaths, 15,900 of which are from food production. Of those, 80% are attributable to animal-based foods, both directly from animal production and indirectly from growing animal feed. On-farm interventions can reduce PM2.5-related mortality by 50%, including improved livestock waste management and fertilizer application practices that reduce emissions of ammonia, a secondary PM2.5 precursor, and improved crop and animal production practices that reduce primary PM2.5 emissions from tillage, field burning, livestock dust, and machinery. Dietary shifts toward more plant-based foods that maintain protein intake and other nutritional needs could reduce agricultural air quality-related mortality by 68 to 83%. In sum, improved livestock and fertilization practices, and dietary shifts could greatly decrease the health impacts of agriculture caused by its contribution to reduced air quality.

Cultured Meat: Promises and Challenges

Cultured meat involves producing meat from animal cells, not from slaughtered animals. This innovation has the potential to revolutionize the meat industry, with wide implications for the environment, health and animal welfare. The main purpose of this paper is to stimulate some economic research on cultured meat. In particular, this paper includes a prospective discussion on the demand and supply of cultured meat. It also discusses some early results on the environmental impacts of cultured meat, emphasizing the promises (e.g., regarding the reduction in land use) but also the uncertainties. It then argues that cultured meat is a moral improvement compared to conventional meat. Finally, it discusses some regulatory issues, and the need for more public support to the innovation.

Fossil Energy Use, Climate Change Impacts, and Air Quality-Related Human Health Damages of Conventional and Diversified Cropping Systems in Iowa, USA

Cropping system diversification can reduce the negative environmental impacts of agricultural production, including soil erosion and nutrient discharge. Less is known about how diversification affects energy use, climate change, and air quality, when considering farm operations and supply chain activities. We conducted a life cycle study using measurements from a nine-year Iowa field experiment to estimate fossil energy (FE) use, greenhouse gas (GHG) emissions, PM_2.5-related emissions, human health impacts, and other agronomic and economic metrics of contrasting crop rotation systems and herbicide regimes. Rotation systems consisted of 2-year corn-soybean, 3-year corn-soybean-oat/clover, and 4-year corn-soybean-oat/alfalfa-alfalfa systems. Each was managed with conventional and low-herbicide treatments. FE consumption was 56% and 64% lower in the 3-year and 4-year rotations than in the 2-year rotation, and GHG emissions were 54% and 64% lower. Diversification reduced combined monetized damages from GHG and PM_2.5-related emissions by 42% and 57%. Herbicide treatment had no significant impact on environmental outcomes, while corn and soybean yields and whole-rotation economic returns improved significantly under diversification. Results suggest that diversification via shifting from conventional corn-soybean rotations to longer rotations with small grain and forage crops substantially reduced FE use, GHG emissions, and air quality damages, without compromising economic or agronomic performance.

Infectious Diseases and Meat Production

Most infectious diseases in humans originate from animals. In this paper, we explore the role of animal farming and meat consumption in the emergence and amplification of infectious diseases. First, we discuss how meat production increases epidemic risks, either directly through increased contact with wild and farmed animals or indirectly through its impact on the environment (e.g., biodiversity loss, water use, climate change). Traditional food systems such as bushmeat and backyard farming increase the risks of disease transmission from wild animals, while intensive farming amplifies the impact of the disease due to the high density, genetic proximity, increased immunodeficiency, and live transport of farmed animals. Second, we describe the various direct and indirect costs of animal-based infectious diseases, and in particular, how these diseases can negatively impact the economy and the environment. Last, we discuss policies to reduce the social costs of infectious diseases. While existing regulatory frameworks such as the "One Health" approach focus on increasing farms' biosecurity and emergency preparedness, we emphasize the need to better align stakeholders' incentives and to reduce meat consumption. We discuss in particular the implementation of a "zoonotic" Pigouvian tax, and innovations such as insect-based food or cultured meat.

How to transition to reduced-meat diets that benefit people and the planet

Overwhelming evidence shows that overconsumption of meat is bad for human and environmental health and that moving towards a more plant-based diet is more sustainable. For instance, replacing beef with beans in the US could free up 42% of US cropland and reduce greenhouse gas emissions by 334 mmt, accomplishing 75% of the 2020 carbon reduction target. We summarise the evidence on how overconsumption of meat affects social, environmental and economic sustainability. We highlight the social, environmental and economic effectiveness of a range of dietary interventions that have been tested to date. Because meat eating is embedded within complex cultural, economic, and political systems, dietary shifts to reduce overconsumption are unlikely to happen quickly and a suite of sustained, context-specific interventions is likely to work better than brief, one-dimensional approaches. We conclude with key actions needed by global leaders in politics, industry and the health sector that could help aide this dietary transformation to benefit people and the planet.

Twenty Key Challenges in Environmental and Resource Economics

Economic and ecological systems are closely interlinked at a global and a regional level, offering a broad variety of important research topics in environmental and resource economics. The successful identification of key challenges for current and future research supports development of novel theories, empirical applications, and appropriate policy designs. It allows establishing a future-oriented research agenda whose ultimate goal is an efficient, equitable, and sustainable use of natural resources. Based on a normative foundation, the paper aims to identify fundamental topics, current trends, and major research gaps to motivate further development of academic work in the field.