Most amyotrophic lateral sclerosis (ALS) cases are sporadic (∼90%) and environmental exposures are implicated in their etiology. Large industrial facilities are permitted the airborne release of certain chemicals with hazardous properties and report the amounts to the US Environmental Protection Agency (EPA) as part of its Toxics Release Inventory (TRI) monitoring program. The objective of this project was to identify industrial chemicals released into the air that may be associated with ALS etiology. We geospatially estimated residential exposure to contaminants using a de-identified medical claims database, the SYMPHONY Integrated Dataverse®, with ∼26,000 nationally distributed ALS patients, and non-ALS controls matched for age and gender. We mapped TRI data on industrial releases of 523 airborne contaminants to estimate local residential exposure and used a dynamic categorization algorithm to solve the problem of zero-inflation in the dataset. In an independent validation study, we used residential histories to estimate exposure in each year prior to diagnosis. Air releases with positive associations in both the SYMPHONY analysis and the spatio-temporal validation study included styrene (false discovery rate (FDR) 5.4e-5), chromium (FDR 2.4e-4), nickel (FDR 1.6e-3), and dichloromethane (FDR 4.8e-4). Using a large de-identified healthcare claims dataset, we identified geospatial environmental contaminants associated with ALS. The analytic pipeline used may be applied to other diseases and identify novel targets for exposure mitigation. Our results support the future evaluation of these environmental chemicals as potential etiologic contributors to sporadic ALS risk.

Little is known about the main sources of ambient particulate matter (PM) in the 22 Eastern Mediterranean Region (EMR) countries. We designed this study to systematically review all published and unpublished source apportionment (SA), identification and characterization studies as well as emission inventories in the EMR. Of 440 articles identified, 82 (11 emission inventory ones) met our inclusion criteria for final analyses. Of 22 EMR countries, Iran with 30 articles had the highest number of studies on source specific PM followed by Pakistan (n = 15 articles) and Saudi Arabia (n = 8 papers). By contrast, there were no studies in Afghanistan, Bahrain, Djibouti, Libya, Somalia, Sudan, Syria, Tunisia, United Arab Emirates and Yemen. Approximately 72% of studies (51) were published within a span of 2015–2021.48 studies identified the sources of PM₂.₅ and its constituents. Positive matrix factorization (PMF), principal component analysis (PCA) and chemical mass balance (CMB) were the most common approaches to identify the source contributions of ambient PM. Both secondary aerosols and dust, with 12–51% and 8–80% (33% and 30% for all EMR countries, on average) had the greatest contributions in ambient PM₂.₅. The remaining sources for ambient PM₂.₅, including mixed sources (traffic, industry and residential (TIR)), traffic, industries, biomass burning, and sea salt were in the range of approximately 4–69%, 4–49%, 1–53%, 7–25% and 3–29%, respectively. For PM₁₀, the most dominant source was dust with 7–95% (49% for all EMR countries, on average). The limited number of SA studies in the EMR countries (one study per approximately 9.6 million people) in comparison to Europe and North America (1 study per 4.3 and 2.1 million people respectively) can be augmented by future studies that will provide a better understanding of emission sources in the urban environment.

Cities constitute an important source of greenhouse gases, but few results originating from long-term, direct CO₂ emission monitoring efforts have been reported. In this study, CO₂ emissions were quasi-continuously measured in an urban center in Sakai, Osaka, Japan by the eddy covariance method from 2010 to 2021. Long-term CO₂ emissions reached 22.2 ± 2.0 kg CO₂ m⁻² yr⁻¹ from 2010 to 2019 (± denotes the standard deviation) in the western sector from the tower representing the densely built-up area. Throughout the decade, the annual CO₂ emissions remained stable. According to an emission inventory, traffic emissions represented the major source of CO₂ emissions within the flux footprint. The interannual variations in the annual CO₂ flux were positively correlated with the mean annual traffic counts at two highway entrances and exits. The CO₂ emissions decreased suddenly, by 32% ± 3.1%, in April and May 2020 during the period in which the first state of emergency associated with COVID-19 was declared. The annual CO₂ emissions also decreased by 25% ± 3.1% in 2020. Direct long-term observations of CO₂ emissions comprise a useful tool to monitor future emission reductions and sudden disruptions in emissions, such as those beginning in 2020 during the COVID-19 pandemic.

Based on the wind erosion equation and the use of moderate resolution imaging spectroradiometer (MODIS) satellite remote sensing data combined with parameter normalization processing, an optimized high spatial-temporal resolution soil fugitive dust (SFD) emission inventory compiling method was proposed in this study. The "2 + 26" cities in northern China, where heavy pollution frequently occurs, were used as a case study. Using the optimized method, we estimated that the PM₅₀, PM₁₀, and PM₂.₅ emissions from SFD of "2 + 26" cities in 2018 were 2,014,927, 1,007,463, and 151,120 tons, respectively. The dust emissions and emission factors of each city presented significant differences and were generally of a greater level in high-latitude areas (such as cities in Hebei Province) than in low-latitude areas (such as cities in Henan and Shandong Province). Moreover, with an increase in latitude, vegetation cover factors generally exhibit an upward trend, while temperature and rainfall exhibit a downward trend. The dust emissions in the different months showed significant differences. The total dust emission reached the highest level in "late winter–early spring" season (February to April), and the monthly emission accounted for 15–17% of the annual emissions. While in the "summer–autumn" season (July to November), it is the lowest level of the whole year, monthly emissions accounted for 3–5% of the annual emissions. The emission inventory method proposed in this study can provide a reference for dust emission assessment and further pollution prevention and control work.

Alberta’s oil sands tailings ponds are suspected to be a source of fugitive emissions of polycyclic aromatic compounds (PACs) to the atmosphere. Here we report, for the first time, fluxes of 6 parent and 21 alkylated PACs based on the measured co-located air and water concentrations using a two-film fugacity-based model (FUG), an inverse dispersion model (DISP) and a simple box model (BOX). Air samples were collected at the Suncor Tailings Pond 2/3 using a high volume air sampler from the “pond” and towards the pond (“non-pond”) directions separately. Mean ∑₂₇PACs in air from the “pond” direction was greater than the “non-pond” direction by a factor of 17. Water-air fugacity ratio of 20 PACs quantifiable in water indicated net volatilization from water. Dispersion and box model results also indicated upward fluxes of 22 PACs. Correlation between the estimated flux results of BOX and DISP model was statistically significant (r = 0.99 and p < 0.05), and correlation between FUG and DISP results ranged from 0.54 to 0.85. In this first-ever assessment of PAC fluxes from tailings pond, the three models confirmed volatilization fluxes of PACs indicating Suncor Tailings Pond 2/3 is a source of PAC emissions to the atmosphere. This study addressed a key data gap identified in the Joint Oil Sands Monitoring Emissions Inventory Compilation Report (Government of Alberta and Canada, 2016) which is the lack of consistent real-world tailings pond fugitive emission monitoring of organic chemicals. Our findings highlight the need for measurements from other tailings ponds to determine their overall contribution in releasing PACs to the atmosphere. This paper presents a practical method for estimating PAC emissions from other tailings ponds, which can provide a better understanding of these fugitive emissions, and thereby help to improve the overall characterization of emissions in the oil sands region.

High concentrations of ground-level ozone affect human health, plants, and animals. Reducing ozone pollution in rural regions, where local emissions are already low, poses challenge. We use meteorological back-trajectories, air quality model sensitivity analysis, and satellite remote sensing data to investigate the ozone sources in Yuma, Arizona and find strong international influences from Northern Mexico on 12 out of 16 ozone exceedance days. We find that such exceedances could not be mitigated by reducing emissions in Arizona; complete removal of state emissions would reduce the maximum daily 8-h average (MDA8) ozone in Yuma by only 0.7% on exceeding days. In contrast, emissions in Mexico are estimated to contribute to 11% of the ozone during these exceedances, and their reduction would reduce MDA8 ozone in Yuma to below the standard. Using satellite-based remote sensing measurements, we find that emissions of nitrogen oxides (NOₓ, a key photochemical precursor of ozone) increase slightly in Mexico from 2005 to 2016, opposite to decreases shown in the bottom-up inventory. In comparison, a decrease of NOₓ emissions in the US and meteorological factors lead to an overall of summer mean and annual MDA8 ozone in Yuma (by ∼1–4% and ∼3%, respectively). Analysis of meteorological back-trajectories also shows similar transboundary transport of ozone at the US-Mexico border in California and New Mexico, where strong influences from Northern Mexico coincide with 11 out of 17 and 6 out of 8 ozone exceedances. 2020 is the final year of the U.S.-Mexico Border 2020 Program, which aimed to reduce pollution at border regions of the US and Mexico. Our results indicate the importance of sustaining a substantial cooperative program to improve air quality at the border area.

Gaining insight into the response of surface ozone (O₃) formation to its precursors plays an important role in the policy-making of O₃ pollution control. However, the real atmosphere is an open and dissipative system, and its complexity poses a great challenge to the study of nonlinear relations between O₃ and its precursors. At present, model-based methods based on reductionism try to restore the real atmospheric photochemical system, by coupling meteorological model and chemical transport model in temporal and spatial resolution completely. Nevertheless, large inconsistencies between predictions and true values still exist, due to the great uncertainty originated from emission inventory, photochemical reaction mechanism and meteorological factors. Recently, based on field observations, some nonlinear methods have successfully revealed the complex emergent properties (long-term persistence, multi-fractal, etc) in coupling correlation between O₃ and its precursors at different time scales. The emergent properties are closely associated with the intrinsic dynamics of atmospheric photochemical system. Taking them into account when building O₃ prediction model, is helpful to reduce the uncertainty in the results. Nonlinear methods (fractal, chaos, etc) based on holism can give new insights into the nonlinear relations between O₃ and its precursors. Changes of thinking models in methodology are expected to improve the precision of forecasting O₃ concentration. This paper has reviewed the advances of different methods for studying the sensitivity of O₃ formation to its precursors during the past few decades. This review highlights that it is necessary to incorporate the emergent properties obtained by nonlinear methods into the modern models, for assessing O₃ formation under combined air pollution environment more accurately. Moreover, the scaling property of coupling correlation detected in the real observations of O₃ and its precursors could be used to test and improve the simulation performance of modern models.

China has been famous for its porcelains for millennia, and the combustion processes of porcelain production emit substantial amounts of air pollutants, which have not been well understood. This study provided firsthand data of air pollutant emissions from biomass porcelain kilns. The emission factor of PM₂.₅ was 0.95 ± 1.23 g/kg during the entire combustion cycle, lower than that of biomass burning in residential stoves and coal burning in brick kilns, attributed to the removal effects of the long-distance transport in dragon kilns. The temporal trend of particle pollutants, including particulate matters (PMs) and particulate polycyclic aromatic hydrocarbons (PAHs) (low at ignition phase and high at the end) again indicated the removal effects of the special structure, while gaseous pollutants, such as gaseous PAHs, exhibited the opposite result. The GWC₁₀₀ was estimated as 1.4 × 10⁶ and 0.5 × 10⁶ kg CO₂e/yr for the scenarios in which 50% and 100% of the wood was renewable, respectively. The GWC₁₀₀ of dragon kilns is nearly equal to that of 745 households using wood-fueled stoves. These results indicate the necessity of pollution controls for biomass porcelain kilns to estimate the emission inventory and climate change.

Vehicular emissions contribute significantly to air pollution, and the number of vehicles in use is continuing to rise. Policymakers thus need to formulate vehicular emission reduction policies to improve urban air-quality. This study used different vehicle control scenarios to predict the associated potential of mitigating carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxide (NOₓ), particulate matter with an aerodynamic diameter less than 2.5 μm (PM₂.₅), and particulate matter with an aerodynamic diameter less than 10 μm (PM₁₀) in Xi’an China, in 2020 and 2025. One business-as-usual scenario and six control scenarios were established, and vehicular emission inventory was developed according to each scenario. The results revealed that eliminating high-emission vehicles and optimizing after-treatment devices would effectively reduce vehicular emissions. In addition, increasing the number of alternative fuel vehicles, restraining vehicle use, and restraining the growth of the vehicle population would all have certain effects on CO and VOCs emissions, but the effects would not be significant for NOx, PM₂.₅, and PM₁₀. The results also indicated that if all control measures were stringently applied together, emissions of CO, VOCs, NOₓ, PM₂.₅, and PM₁₀ would be reduced by 51.66%, 51.58%, 30.19%,71.12%, and 71.81% in 2020, and 53.55%, 51.44%, 19.09%, 54.88%, and 55.51%, in 2025, respectively.

Copper-containing antifouling paints (AFP) are widely used for leisure boat maintenance. Cu emitted from AFP into German surface water bodies has been suggested to be a significant source of heavy metal pollution, threatening water quality. We developed two scenarios to model Cu emissions from AFP applied on leisure boats on national scale, which allow identifying regional hotspots. The top-down approach (scenario A) was based on a previous study on national AFP consumption, while in the bottom-up approach (scenario B), median and interquartile range of Cu release rates depending on salinity conditions were considered for emission estimation. Both scenarios clearly highlighted the locally high emission pressure on inland waters in popular watersport regions, identifying these as requiring intense protection. Scenario B generally predicted lower Cu emissions (sea: 11.05–25.53 t a⁻¹, inland: 14.15–34.59 t a⁻¹) than scenario A (sea: 22.53 t a⁻¹, inland: 47.97 t a⁻¹). To evaluate their relevance, scenario results were compared to emissions modelled with MoRE (Modelling of Regionalized Emissions), which is used as reporting tool on substance emissions by Germany. According to scenarios A and B, the emission from AFP accounted for 13 % and 4–9 % of the total Cu emissions into inland waters in 2016, respectively. Scenario results were similar or higher than other emission pathways such as industrial direct dischargers. Thus, we consider Cu emissions from AFP as a significant pathway to be included in the MoRE emission inventory. We recommend scenario B for implementation as it allows a more flexible adaptation for future modelling.