The Tijuana River Valley along the US-Mexico border faces a severe environmental and public health crisis from contamination of the Tijuana River with untreated sewage, urban runoff, and industrial waste, leading to water and air exposure concerns. The County of San Diego Health and Human Services Agency (CoSD HHSA) and the Centers for Disease Control and Prevention (CDC) conducted 189 household-level surveys to assess experiences with the crisis and address concerns. Survey results revealed that ~96% of households were concerned about the crisis. Top needs included requests for action (64.3%) and air (38.2%) and water (27.1%) quality improvement. Subsequently, CoSD HHSA developed data-driven outreach programs and resources (e.g., provision of air filters), enhanced messaging (e.g., providing educational materials, promoting behavioral health hotlines) related to the crisis to address household concerns, and shared the results with other agencies (e.g., the water board) to promote further action and safety messaging.

Air pollution is a major environmental and public health risk globally leading to millions of premature deaths annually and negative economic effects. One of the key challenges in managing air quality is the availability of actionable spatial air quality data. The sparse networks or absence of air quality monitoring stations in many places means that there are limited data and information on air pollution in places without coverage. The spatial prediction of air quality can contribute to increasing data access for locations without air quality monitoring, ultimately improving awareness of the risk of air pollution exposure for vulnerable people. In this study, we investigated the air quality prediction task in two cities in Uganda (i.e., Jinja and Kampala), with unique geographic and economic contexts. Primarily, we used Gaussian processes to predict the PM$ {}_{2.5} $ levels in the two cities, selected because of their relative importance in the country and their varying characteristics. We achieved promising results with an average root-mean-square error (RMSE) of 18.32 μg/m3 and 16.88 μg/m3 in Kampala and Jinja, respectively. These results provide valuable insights into the air quality profiles of two urban sub-Saharan cities with different demographics, which can in turn aid in decision-making for targeted actions at different levels.

Ambient air pollution remains a global challenge, with adverse impacts on health and the environment. Addressing air pollution requires reliable data on pollutant concentrations, which form the foundation for interventions aimed at improving air quality. However, in many regions, including the United Kingdom, air pollution monitoring networks are characterized by spatial sparsity, heterogeneous placement, and frequent temporal data gaps, often due to issues such as power outages. We introduce a scalable data-driven supervised machine learning model framework designed to address temporal and spatial data gaps by filling missing measurements within the United Kingdom. The machine learning framework used is LightGBM, a gradient boosting algorithm based on decision trees, for efficient and scalable modeling. This approach provides a comprehensive dataset for England throughout 2018 at a 1 km2 hourly resolution. Leveraging machine learning techniques and real-world data from the sparsely distributed monitoring stations, we generate 355,827 synthetic monitoring stations across the study area. Validation was conducted to assess the model’s performance in forecasting, estimating missing locations, and capturing peak concentrations. The resulting dataset is of particular interest to a diverse range of stakeholders engaged in downstream assessments supported by outdoor air pollution concentration data for nitrogen dioxide (NO2), Ozone (O3), particulate matter with a diameter of 10 μm or less (PM10), particulate matter with a diameter of 2.5 μm or less PM2.5, and sulphur dioxide (SO2), at a higher resolution than was previously possible.

The increasing size and severity of wildfires across the western United States have generated dangerous levels of PM2.5 concentrations in recent years. In a changing climate, expanding the use of prescribed fires is widely considered to be the most robust fire mitigation strategy. However, reliably forecasting the potential air quality impact from prescribed fires, which is critical in planning the prescribed fires’ location and time, at hourly to daily time scales remains a challenging problem. In this paper, we introduce a spatio-temporal graph neural network (GNN)-based forecasting model for hourly PM2.5 predictions across California. Utilizing a two-step approach, we use our forecasting model to predict the net and ambient PM2.5 concentrations, which are used to estimate wildfire contributions. Integrating the GNN-based PM2.5 forecasting model with simulations of historically prescribed fires, we propose a novel framework to forecast their air quality impact. This framework determines that March is the optimal month for implementing prescribed fires in California and quantifies the potential air quality trade-offs involved in conducting more prescribed fires outside the peak of the fire season.

We introduce a novel human-centric deep reinforcement learning recommender system designed to co-optimize energy consumption, thermal comfort, and air quality in commercial buildings. Existing approaches typically optimize these objectives separately or focus solely on controlling energy-consuming building resources without directly engaging occupants. We develop a deep reinforcement learning architecture based on multitask learning with humans-in-the-loop and demonstrate how it can jointly learn energy savings, comfort, and air quality improvements for different building and occupant actions. In addition to controlling typical building resources (e.g., thermostat setpoint), our system provides real-time actionable recommendations that occupants can take (e.g., move to a new location) to co-optimize energy, comfort, and air quality. Through real deployments across multiple commercial buildings, we show that our multitask deep reinforcement learning recommender system has the potential to reduce energy consumption by up to 8% in energy-focused optimization, improve all objectives by 5–10% in joint optimization, and improve thermal comfort by up to 21% in comfort and air quality-focused optimization compared to existing solutions.

Urban inhabitants spend upwards of 90% of their time indoors where building design and mechanical air-handling systems negatively impact air quality, microbiome diversity and health outcomes. Urban bioremediation infrastructure designed to improve indoor environmental quality by drawing air through photosynthesizing plants and metabolically diverse rhizospheres have been investigated since the 1960s; however, in-depth analysis of the potential impacts on indoor environments is required: (1) although recent evidence has illustrated human microbiome alteration and associated health benefits related to exposure to green wall systems, the mechanism(s) of diversification have not yet been established, (2) microbial metabolism and airborne chemical dynamics are extraordinarily complex and hypotheses pertaining to rhizosphere microorganisms metabolizing pollutants require more attention. To explore these areas, we applied a shotgun metagenomic approach to quantify microbial diversity and establish preliminary metabolic profiles within active green wall modules spanning a range of growth media and plant selections. Results indicate that fundamental design decisions, including hydroponic vs. organic growth media, support rhizosphere microbiomes with distinct diversity and metabolic profiles which could impact system performance. The described relationships indicate fundamental green infrastructure design represents an opportunity to “grow” indoor microbial diversity and metabolisms with potential benefits for human pollutant exposure and health outcomes.

Reliance upon fossil fuels and limited greenspace contribute to poor indoor and outdoor air quality and adverse health outcomes, particularly in communities of color. This article describes justice-informed public health and legal interventions to increase access to greenspace and accelerate the transitions to renewable energy and away from gas appliances.

Exposure to ground-level ozone is a concern for both humans and vegetation, so accurate prediction of ozone time series is of great importance. However, conventional as well as emerging methods have deficiencies in predicting time series when a superposition of differently pronounced oscillations on various time scales is present. In this paper, we propose a meteorologically motivated filtering method of time series data, which can separate oscillation patterns, in combination with different multibranch neural networks. To avoid phase shifts introduced by using a causal filter, we combine past observation data with a climatological estimate about the future to be able to apply a noncausal filter in a forecast setting. In addition, the forecast in the form of the expected climatology provides some a priori information that can support the neural network to focus not merely on learning a climatological statistic. We apply this method to hourly data obtained from over 50 different monitoring stations in northern Germany situated in rural or suburban surroundings to generate a prediction for the daily maximum 8-hr average values of ground-level ozone 4 days into the future. The data preprocessing with time filters enables simpler neural networks such as fully connected networks as well as more sophisticated approaches such as convolutional and recurrent neural networks to better recognize long-term and short-term oscillation patterns like the seasonal cycle and thus leads to an improvement in the forecast skill, especially for a lead time of more than 48 hr, compared to persistence, climatological reference, and other reference models.

Economic and health benefits assessments of air quality changes often quantify and report changes in deaths at a given point in time. The typical approach uses a method that attributes air pollution-related health impacts to a single year air quality change (or “pulse”). The perspective on benefits from these static pulse analyses can be enhanced by conducting a dynamic population assessment using life tables. Such analyses can provide a richer characterization of health risks across a population over a multiyear time horizon. In this article, we use the life table approach to quantify cumulative counts of reductions in PM-attributable deaths and life-years gained due to overlapping impacts of PM2.5 changes over a multiyear period, using case studies of air quality improvements in the USA and Chile. Our comparison of health risk and economic valuation for the two approaches shows life table analysis can be a valuable adjunct analysis to the pulse approach though both come with their own set of uncertainties and limitations. If applied jointly, they provide a broader characterization of how air quality actions can change populations in terms of life-years lost, life expectancy, and age structure. The value of these metrics is illustrated using case studies with dramatically different air quality reduction trajectories.

This paper explores the short-run impact of work resumption, extensively launched on February 10, 2020 in China, on air quality after the subsiding of COVID-19. Utilizing the data of 1012 air-quality monitoring sites in 233 cities derived from the Real-time Release Air Quality Platform and the difference-in-differences method, we find that alternative measures of air quality index in non-Hubei provinces increase significantly, compared with those in Hubei province which was temporarily not allowed work resumption due to the severity of epidemic. Specifically, our results reveal a rise in AQI of 11.28 per cent, in PM2.5 of 12.47 per cent, in PM10 of 10.49 per cent, and in NO2 of 23.64 per cent, relative to the baseline mean. Moreover, the deterioration of air quality is found to be caused by intracity rather than intercity migration.

As far as local governments are responsible for the practical implementation of many European Union (EU) policies, they codetermine member states’ EU compliance records and the fate of EU legislation. Yet, they do so in remarkably different ways, as exemplified by the variegated implementation of the Ambient Air Quality Directive 2008/50/EC by Dutch municipalities. Taking guidance from the literature on EU compliance, in this article we explain the differences in local implementation performance based on the political and managerial approaches. Understanding which of the two approaches drives different local responses to EU policy bears consequences for the appropriate remedy for nonimplementation. Four municipalities were purposefully selected along with the two-by-two implementation performance scoring matrix in the realm of air quality. A comparative within-case analysis specifies how political explanations outweigh managerial explanations in accounting for variation in implementation performance and distils ‘policy saliency’ as the driving causal mechanism.

This article investigates whether residents of Mexico City value air quality. Our results suggest that air quality improvement in PM10 is equivalent to a marginal willingness to pay (MWTP) of US$440.31 per property for the period 2006–2013. The corresponding MWTP for PM2.5 is US$880.63, for O3 is US$623.78, and for SO2 is as much as US$2091.50. These estimates are considerably larger in magnitude compared to the few other studies in similar settings. As a percentage of annual household income, these represent 2.44 per cent for PM10, 4.88 per cent for PM2.5, 3.46 per cent for O3 and 11.59 per cent for SO2. Our estimates of land value–pollution elasticities for PM10 (−0.26 and − 0.58) are within range of hedonic estimates for total suspended particulate matter in US cities around the 1970s. The corresponding elasticities range from − 0.55 to − 0.84 for PM2.5, from − 0.06 to − 0.49 for O3 and from − 0.11 to − 0.34 for SO2.

Atmospheric flow equations govern the time evolution of chemical concentrations in theatmosphere. When considering gas and particle phases, the underlying partial differentialequations involve advection and diffusion operators, coagulation effects, and evaporationand condensation phenomena between the aerosol particles and the gas phase. Operatorsplitting techniques are generally used in global air quality models. When consideringorganic aerosol particles, the modeling of the thermodynamic equilibrium of each particleleads to the determination of the convex envelope of the energy function. Two strategiesare proposed to address the computation of convex envelopes. The first one is based on aprimal-dual interior-point method, while the second one relies on a variationalformulation, an appropriate augmented Lagrangian, an Uzawa iterative algorithm, and finiteelement techniques. Numerical experiments are presented for chemical systems ofatmospheric interest, in order to compute convex envelopes in various spacedimensions.

With the passage of the 1990 Clean Air Act came the responsibility to monitor and control particulates in the size range 10 μ and smaller (PM10). Many urban areas, particularly in the western U.S., have experienced concentrations of fugitive dust particulates from upwind sources that exceed the federal health standards. Often a significant amount of this material is generated upwind on agricultural fields, and then is entrained and transported in the regional air mass, thus degrading the air quality in downwind urban regions. Current technology cannot adequately quantify the fugitive dust emitted and transported from agricultural sources, nor specify adequate control methods. A comprehensive research plan recently was developed and initia ted for the Columbia Plateau of eastern Washington State that involves multiple disciplines and several state and federal agencies. This research has several components: characterizing the soil, vegetation and climate in a region of 136,000 km2; developing wind erosion and fugitive dust emission relationships for individual farm fields; developing and applying transport-dispersion-deposition models of the region; selecting and testing farm-level control strategies; and providing public information to both the urban and farm communities for understanding the problem and developing management plans. Simultaneous receptor analyses and public health research combine to make this a comprehensive regional research effort on fugitive dust emissions and impacts.