Explore the vast range of titles available.

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations-the air pollution outcome generally causing the largest monetized health damages-attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons run here, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of -17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.

Significance Our assessment of the life cycle air quality impacts on human health of 10 alternatives to conventional gasoline vehicles finds that electric vehicles (EVs) powered by electricity from natural gas or wind, water, or solar power are best for improving air quality, whereas vehicles powered by corn ethanol and EVs powered by coal are the worst. This work advances the current debate over the environmental impacts of conventional versus alternative transportation options by combining detailed spatially and temporally explicit emissions inventories with state-of-the-science air quality impact analysis using advanced chemical transport modeling. Our results reinforce previous findings that air quality-related health damages from transportation are generally comparable to or larger than climate change-related damages. Commonly considered strategies for reducing the environmental impact of light-duty transportation include using alternative fuels and improving vehicle fuel economy. We evaluate the air quality-related human health impacts of 10 such options, including the use of liquid biofuels, diesel, and compressed natural gas (CNG) in internal combustion engines; the use of electricity from a range of conventional and renewable sources to power electric vehicles (EVs); and the use of hybrid EV technology. Our approach combines spatially, temporally, and chemically detailed life cycle emission inventories; comprehensive, fine-scale state-of-the-science chemical transport modeling; and exposure, concentration–response, and economic health impact modeling for ozone (O ₃) and fine particulate matter (PM ₂.₅). We find that powering vehicles with corn ethanol or with coal-based or “grid average” electricity increases monetized environmental health impacts by 80% or more relative to using conventional gasoline. Conversely, EVs powered by low-emitting electricity from natural gas, wind, water, or solar power reduce environmental health impacts by 50% or more. Consideration of potential climate change impacts alongside the human health outcomes described here further reinforces the environmental preferability of EVs powered by low-emitting electricity relative to gasoline vehicles.

Fine particulate matter (PM2.5) air pollution exposure is the largest environmental health risk factor in the United States. Here, we link PM2.5 exposure to the human activities responsible for PM2.5 pollution. We use these results to explore “pollution inequity”: the difference between the environmental health damage caused by a racial–ethnic group and the damage that group experiences. We show that, in the United States, PM2.5 exposure is disproportionately caused by consumption of goods and services mainly by the non-Hispanic white majority, but disproportionately inhaled by black and Hispanic minorities. On average, non-Hispanic whites experience a “pollution advantage”: They experience ∼17% less air pollution exposure than is caused by their consumption. Blacks and Hispanics on average bear a “pollution burden” of 56% and 63% excess exposure, respectively, relative to the exposure caused by their consumption. The total disparity is caused as much by how much people consume as by how much pollution they breathe. Differences in the types of goods and services consumed by each group are less important. PM2.5 exposures declined ∼50% during 2002–2015 for all three racial–ethnic groups, but pollution inequity has remained high.

Balancing productivity, profitability, and environmental health is a key challenge for agricultural sustainability. Most crop production systems in the United States are characterized by low species and management diversity, high use of fossil energy and agrichemicals, and large negative impacts on the environment. We hypothesized that cropping system diversification would promote ecosystem services that would supplement, and eventually displace, synthetic external inputs used to maintain crop productivity. To test this, we conducted a field study from 2003–2011 in Iowa that included three contrasting systems varying in length of crop sequence and inputs. We compared a conventionally managed 2-yr rotation (maize-soybean) that received fertilizers and herbicides at rates comparable to those used on nearby farms with two more diverse cropping systems: a 3-yr rotation (maize-soybean-small grain + red clover) and a 4-yr rotation (maize-soybean-small grain + alfalfa-alfalfa) managed with lower synthetic N fertilizer and herbicide inputs and periodic applications of cattle manure. Grain yields, mass of harvested products, and profit in the more diverse systems were similar to, or greater than, those in the conventional system, despite reductions of agrichemical inputs. Weeds were suppressed effectively in all systems, but freshwater toxicity of the more diverse systems was two orders of magnitude lower than in the conventional system. Results of our study indicate that more diverse cropping systems can use small amounts of synthetic agrichemical inputs as powerful tools with which to tune, rather than drive, agroecosystem performance, while meeting or exceeding the performance of less diverse systems.

Reliable estimates of externality costs-such as the costs arising from premature mortality due to exposure to fine particulate matter (PM2.5)-are critical for policy analysis. To facilitate broader analysis, several datasets of the social costs of air quality have been produced by a set of reduced-complexity models (RCMs). It is much easier to use the tabulated marginal costs derived from RCMs than it is to run 'state-of-the-science' chemical transport models (CTMs). However, the differences between these datasets have not been systematically examined, leaving analysts with no guidance on how and when these differences matter. Here, we compare per-tonne marginal costs from ground level and elevated emission sources for each county in the United States for sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3) and inert primary PM2.5 from three RCMs: Air Pollution Emission Experiments and Policy (AP2), Estimating Air pollution Social Impacts Using Regression (EASIUR) and the Intervention Model for Air Pollution (InMAP). National emission-weighted average damages vary among models by approximately 21%, 31%, 28% and 12% for inert primary PM2.5, SO2, NOx and NH3 emissions, respectively, for ground-level sources. For elevated sources, emission-weighted damages vary by approximately 42%, 26%, 42% and 20% for inert primary PM2.5, SO2, NOx and NH3 emissions, respectively. Despite fundamental structural differences, the three models predict marginal costs that are within the same order of magnitude. That different and independent methods have converged on similar results bolsters confidence in the RCMs. Policy analyzes of national-level air quality policies that sum over pollutants and geographical locations are often robust to these differences, although the differences may matter for more source- or location-specific analyzes. Overall, the loss of fidelity caused by using RCMs and their social cost datasets in place of CTMs is modest.

Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM 2.5 ). Designing policies to reduce these deaths relies on air quality modeling for estimating changes in PM 2.5 concentrations from many scenarios at high spatial resolution. However, air quality modeling typically has substantial requirements for computation and expertise, which limits policy design, especially in countries where most PM 2.5 -related deaths occur. Lower requirement reduced-complexity models exist but are generally unavailable worldwide. Here, we adapt InMAP, a reduced-complexity model originally developed for the United States, to simulate annual-average primary and secondary PM 2.5 concentrations across a global-through-urban spatial domain: “Global InMAP”. Global InMAP uses a variable resolution grid, with horizontal grid cell widths ranging from 500 km in remote locations to 4km in urban locations. We evaluate Global InMAP performance against both measurements and a state-of-the-science chemical transport model, GEOS-Chem. Against measurements, InMAP predicts total PM 2.5 concentrations with a normalized mean error of 62%, compared to 41% for GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%–121%, which is sufficient for initial policy assessment and scoping. Global InMAP can be run on a desktop computer; simulations here took 2.6–8.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate policy assessment worldwide, providing a screening tool for reducing air pollution-related deaths where they occur most.

The global food system is essential for the health and wellbeing of society, but is also a major cause of environmental damage. Some impacts, such as on climate change, have been the subject of intense recent inquiry, but others, such as on air quality, are not as well understood. Here, we systematically synthesize the literature to identify the impacts on ambient PM2.5 (particulate matter with diameter ⩽2.5 μm), which is the strongest contributor to premature mortality from exposure to air pollution. Our analysis indicates that the life-cycle of the global food system (pre-production, production, post-production, consumption and waste management) accounts for 58% of anthropogenic, global emissions of primary PM2.5, 72% of ammonia (NH3), 13% of nitrogen oxides (NO x ), 9% of sulfur dioxide (SO2), and 19% of non-methane volatile organic compounds (NMVOC). These emissions result in at least 890 000 ambient PM2.5-related deaths, which is equivalent to 23% of ambient PM2.5-related deaths reported in the Global Burden of Disease Study 2015. Predominant contributors include livestock and crop production, which contribute >50% of food-related NH3 emissions, and land-use change and waste burning, which contribute up to 95% of food-related primary PM2.5 emissions. These findings are largely underestimated given the paucity of data from the post-production and consumption stages, total underestimates in NH3 emissions, lack of sector-scale analysis of PM2.5-related deaths in South America and Africa, and uncertainties in integrated exposure-response functions. In addition, we identify mitigation opportunities—including shifts in food demand, changes in agricultural practices, the adoption of clean and low-energy technologies, and policy actions—that can facilitate meeting food demand with minimal PM2.5 impacts. Further research is required to resolve sectoral-scale, region-specific contributions to PM2.5-related deaths, and assess the efficiency of mitigation strategies. Our review is positioned to inform stakeholders, including scientists, engineers, policymakers, farmers and the public, of the health impacts of reduced air quality resulting from the global food system.

Greenhouse gas emissions associated with pesticide applications against invasive species constitute an environmental cost of species invasions that has remained largely unrecognized. Here we calculate greenhouse gas emissions associated with the invasion of an agricultural pest from Asia to North America. The soybean aphid, Aphis glycines, was first discovered in North America in 2000, and has led to a substantial increase in insecticide use in soybeans. We estimate that the manufacture, transport, and application of insecticides against soybean aphid results in approximately 10.6 kg of carbon dioxide (CO2) equivalent greenhouse gasses being emitted per hectare of soybeans treated. Given the acreage sprayed, this has led to annual emissions of between 6 and 40 million kg of CO2 equivalent greenhouse gasses in the United States since the invasion of soybean aphid, depending on pest population size. Emissions would be higher were it not for the development of a threshold aphid density below which farmers are advised not to spray. Without a threshold, farmers tend to spray preemptively and the threshold allows farmers to take advantage of naturally occurring biological control of the soybean aphid, which can be substantial. We find that adoption of the soybean aphid economic threshold can lead to emission reductions of approximately 300 million kg of CO2 equivalent greenhouse gases per year in the United States. Previous studies have documented that biological control agents such as lady beetles are capable of suppressing aphid densities below this threshold in over half of the soybean acreage in the U.S. Given the acreages involved this suggests that biological control results in annual emission reductions of over 200 million kg of CO2 equivalents. These analyses show how interactions between invasive species and organisms that suppress them can interact to affect greenhouse gas emissions.

The Brazilian Soy Moratorium has effectively reduced forest conversion for soybeans in Amazonia. This has come at the expense of the region's pasturelands, which have increasingly ceded space for compliant soy expansion. The question of extending the policy to the Cerrado, where recent soy expansion has come at the cost of ecologically valuable vegetation, plugs into a wider discussion on how to reconcile competing commodities on finite amounts of cleared area. Innovative management strategies that allow different land uses to coexist are urgently needed. Integrated crop-livestock systems with soybeans (ICLS) rotates beef and soy on the same area, and shows promise as a means to improve production, farmer benefit, and environmental impacts. Here we reconstruct historical land use maps to estimate Cerrado Soy Moratorium outcomes with benchmark years in 2008 and 2014, we then estimate additional production afforded by ICLS implementation between 2008 and 2014. We find that if a 2008 Cerrado Soy Moratorium were in place, 0.7 Mha of 2014 Cerrado soy area would currently be in violation of the policy. Roughly 96% of this acreage is found in Matopiba (82%) and Mato Grosso (14%) states, suggesting that adoption may have slowed recent production in these rapidly transforming soy centers, in contrast to central and southwestern Cerrado where there is more concentrated eligible expansion area. Changing the benchmark to 2014 could have added 0.7 Mha of eligible expansion area, though over 80% of these additions would be in states with the most 2008 eligible area (Distrito Federal, Mato Grosso, Maranhão, Minas Gerais, Mato Grosso do Sul). Meanwhile, ICLS adoption could have added between 4.0 and 32 Mha of new soy land to the study area without additional clearing between 2008 and 2014, though this would depend on rigorous accompanying land zoning policy to guide implementation. The roughly 5 Mha of Cerrado soybean expansion that actually occurred between 2008 and 2014 could have been accommodated on 2008 suitable pasture area given an ICLS rotation frequency of every 6 years or less. Conservation estimates presented here represent the upper limit of what is possible, as our scenario modeling does not account for variables such as leakage, laundering, or rebound effects.

Air pollution from coal-fired electricity generation is an important cause of premature mortality in India. Although pollution-related mortality from the sector has been extensively studied, the relative contribution of individual coal-fired units to the fleet-wide mortality burden remains unclear. Here, we find that emissions from a small number of units drive overall mortality. Units producing just 3.5% of total generation and constituting less than 3% of total capacity result in 25% of annual premature mortality from coal-fired generation. This is a direct consequence of the 200-fold variation that we find in the mortality intensity of electricity generation across units. We use a detailed emissions inventory, a reduced complexity air quality model, and non-linear PM 2.5 concentration-response functions to estimate marginal premature mortality for over 500 units operational in 2019. Absolute annual mortality ranges from less than 1 to over 650 deaths/year across units, and the mortality intensity of generation varies from under 0.002 to 0.43 deaths/GWh. Our findings suggest the potential for large social benefits in the form of reduced PM 2.5 -related premature mortality in India if the highest mortality intensity units are prioritized for the implementation of pollution control technologies or accelerated retirement.