Pollution Mitigation Strategies Research Articles

Influence of regional emission controls on the chemical composition, sources, and size distributions of submicron aerosols: Insights from the 2014 Nanjing Youth Olympic Games

High-intensity emission controls were implemented in Nanjing and in 8 surrounding cities to ensure good air quality during the 2014 summer Youth Olympic Games (YOG). An Aerodyne soot-particle aerosol mass spectrometer (SP-AMS) was deployed at a downwind site of downtown Nanjing to investigate the chemical composition, sources, and size distribution of submicron aerosols (PM1), in response to emission control policies. However, results show that emission controls played a negligible role in reducing PM1 concentration during the YOG period, yet primary precursors such as NOx and SO2 were decreased by 10–20%. Low wind speed, high relative humidity, and high ozone (O3) concentration likely play a significant role in the production and accumulation of the oxygenated organic aerosol (OOA) and the secondary inorganic aerosols (SIA) in summer Nanjing. We propose that long-term regional emission reduction could be a solution for future air pollution mitigation strategies in downwind cities of the YRD region, and that seasonal meteorological characteristics in a specific region should be considered before emission control policies are made.

Citizen science: A way forward in tackling the plastic pollution crisis during and beyond the COVID-19 pandemic

The COVID-19 pandemic has resulted in an unprecedented surge of production, consumption, and disposal of single-use plastics (SUPs) and personal protective equipment (PPE) by the public. This widespread use of mostly plastic items like face masks and disposable gloves has led to global reports of improper disposal of potentially infectious PPE both in our urban and natural environments. Due to international travel restrictions during the pandemic, many research programs targeted at measuring plastic pollution were halted. These disruptions to research programs have stunted the ability to assess the true quantities of SUPs and PPE being mismanaged from the waste stream into the environment. This article calls for increased citizen science participation in collecting plastic pollution data both during and post pandemic. By initiating this dialogue and raising attention to the importance and potential of citizen science data collection, data can be used to develop globally informed plastic pollution mitigation strategies.

Deep learning techniques applied to super-resolution chemistry transport modeling for operational uses

Air quality modeling tools are largely used to assess air pollution mitigation and monitoring strategies. While neural networks (NN) were mostly developed based on observations to derive statistical models at stations, the use of Eulerian chemistry transport models (CTMs) was mainly devoted to air quality predictions over large areas and the evaluation of emission reduction strategies. In this study, we investigate deep learning architectures to create a metamodel of the process oriented CTM CHIMERE and significantly reduce the computing times required for super-resolution simulations. The key point is the selection of input variables and the way to implement them in the NN. We perform a quantitative evaluation of the proposed approaches on a real case-study. The best NN architecture displays very good performances in terms of prediction of pollutant concentrations observed at stations with respect to the raw super-resolution CHIMERE simulation, with a correlation coefficient above 0.95. The best NN is also able to display better performances when compared to observations than the raw high resolution simulation. Currently the model is designed to be used for air quality forecasting and requires improvement for the definition of air quality management strategies.

Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis.

Background: We present a systematic review of studies assessing the association between ambient particulate matter (PM) and premature mortality and the results of a Bayesian hierarchical meta-analysis while accounting for population differences of the included studies. Methods: The review protocol was registered in the PROSPERO systematic review registry. Medline, CINAHL and Global Health databases were systematically searched. Bayesian hierarchical meta-analysis was conducted using a non-informative prior to assess whether the regression coefficients differed across observations due to the heterogeneity among studies. Results: We identified 3248 records for title and abstract review, of which 309 underwent full text screening. Thirty-six studies were included, based on the inclusion criteria. Most of the studies were from China (n = 14), India (n = 6) and the USA (n = 3). PM2.5 was the most frequently reported pollutant. PM was estimated using modelling techniques (22 studies), satellite-based measures (four studies) and direct measurements (ten studies). Mortality data were sourced from country-specific mortality statistics for 17 studies, Global Burden of Disease data for 16 studies, WHO data for two studies and life tables for one study. Sixteen studies were included in the Bayesian hierarchical meta-analysis. The meta-analysis revealed that the annual estimate of premature mortality attributed to PM2.5 was 253 per 1,000,000 population (95% CI: 90, 643) and 587 per 1,000,000 population (95% CI: 1, 39,746) for PM10. Conclusion: 253 premature deaths per million population are associated with exposure to ambient PM2.5. We observed an unstable estimate for PM10, most likely due to heterogeneity among the studies. Future research efforts should focus on the effects of ambient PM10 and premature mortality, as well as include populations outside Asia. Key messages: Ambient PM2.5 is associated with premature mortality. Given that rapid urbanization may increase this burden in the coming decades, our study highlights the urgency of implementing air pollution mitigation strategies to reduce the risk to population and planetary health.

Sources of PM2.5 and its responses to emission reduction strategies in the Central Plains Economic Region in China: Implications for the impacts of COVID-19

The Central Plains Economic Region (CPER) located along the transport path to the Beijing-Tianjin-Hebei area has experienced severe PM2.5 pollution in recent years. However, few modeling studies have been performed on the sources of PM2.5, especially the impacts of emission reduction strategies. In this study, the Nested Air Quality Prediction Model System (NAQPMS) with an online tracer-tagging module was adopted to investigate source sectors of PM2.5 and a series of sensitivity tests were conducted to investigate the impacts of different sector-based mitigation strategies on PM2.5 pollution. The response surfaces of pollutants to sector-based emission changes were built. The results showed that resident-related sector (resident and agriculture), fugitive dust, traffic and industry emissions were the main sources of PM2.5 in Zhengzhou, contributing 49%, 19%, 15% and 13%, respectively. Response surfaces of pollutants to sector-based emission changes in Henan revealed that the combined reduction of resident-related sector and industry emissions efficiently decreased PM2.5 in Zhengzhou. However, reduced emissions in only the Henan region barely satisfied the national air quality standard of 75 μg/m3, whereas a 50%–60% reduction in resident-related sector and industry emissions over the whole region could reach this goal. On severely polluted days, even a 60% reduction in these two sectors over the whole region was insufficient to satisfy the standard of 75 μg/m3. Moreover, a reduction in traffic emissions resulted in an increase in the O3 concentration. The results of the response surface method showed that PM2.5 in Zhengzhou decreased by 19% in response to the COVID-19 lockdown, which approached the observed reduction of 21%, indicating that the response surface method could be employed to study the impacts of the COVID-19 lockdown on air pollution. This study provides a scientific reference for the formulation of pollution mitigation strategies in the CPER.

Present and Future Prospect of Algae: A Potential Candidate for Sustainable Pollution Mitigation

Pollution control and mitigation are critical to protect the ecosystem and make everyone's life safer and healthier. Different pollution mitigation strategies and measures are implemented to remove pollutants, which broadly involve physical, chemical, and biological methods. Biological methods are found to be more sustainable, effective, and eco-friendlier than the other two methods. These methods mainly use microbes like bacteria, fungi, algae, and plants, and their products like enzymes and metabolic products to remove pollutants. Due to their unique photosynthetic ability and simple growth requirements, Algae can be grown using simpler components like CO2, sunlight, and media, making them a potential candidate to be used as a pollution mitigator. Algae can indicate and remove pollutants like CO2, SO2, NO2, and particulate matter from the air; these pollutants and particulate matter are either used for their growth or these are accumulated inside them.. Algal species have shown the efficient removal of heavy metals, organic pollutants, explosives, petroleum contaminants, pesticides, polycyclic aromatic hydrocarbons (PAHs), and plastics from different water sources. There is a lot of scope in using algae to remove organic and inorganic pollutants in wastewater treatment plants. Algae hold great potential to remove radioactive pollutants from natural resources and involve removal mechanisms like biosorption and bioaccumulation. Algae can be used with different adsorbent materials to develop adsorption systems for the adsorption of radionuclides and heavy metals. This review elucidates different algal species, their cultural conditions, the removal efficiency of different types of pollutants from the air, water, soil, and their role in genetic engineering and the algae's potential for waste mitigation.

Analytics in Extracting Intelligence from IoT-Based Sensors in Transportation, Healthcare and Natural Gas Detection

Networking of sensors and the development of sensor systems have become routine technological endeavors. The advent of Internet-of-Things (IoT) technologies has enabled an expanding matrix of applications for integration of specialized sensor systems. The focus on innovation is now on driving costs down, while increasing performance. Additive manufacturing techniques and topological improvements are key drivers of innovation for sensors.As sensors and sensor systems transform their capabilities from (strictly) data generation and gathering, the focus now shifts on how to increase the utility of the sensor and sensor system.SensorComm defines the utility of a sensor as its ability to provide intelligence. Intelligence comes from the collection of data. Through data-analytics, information can be extracted. The final step of interpreting the information is through the generation of intelligence. Off-the-shelf tools are available that include machine learning, artificial intelligence, and even toolboxes in platforms like MATLAB, all of which have changed the way we obtain information. The next step is obtaining the intelligence.SensorComm has applied this methodology of intelligence extraction in three areas: 1) transportation 2) healthcare and now 3) natural gas monitoring/detection.In transportation, we will describe how intelligence is obtained from SensorComm’s IoT-based mobile pollution monitoring system. Installed at the tailpipe, Wi-NOx™ captures the real-time pollution footprint of vehicles. The system extracts, analyzes and transforms NOx emissions data into real-time information providing transportation managers with business intelligence that leads to operational efficiencies and measurable savings. Wi-NOx™ is the cornerstone of a global pollution mitigation strategy that enables fuel savings, emissions reduction, vehicle optimization and extended asset life (to responsibly delay significant infrastructure investments like electrification).The Wi-NOx™ system was developed to monitor emissions from vehicles. Recently, we have modified it to remotely monitor patients in healthcare applications, and now, to monitor natural gas for oil & gas applications.In healthcare, Sensorcomm has developed a wrist-worn temperature monitoring system for COVID-19 detection using the Data-Centric Care™ model. Data-Centric Care™ focuses on data to provide the necessary intelligence to correctly assess the situation. The system uses (near) continuous temperature monitoring of individuals to identify at-risk persons who may or may not be symptomatic (for COVID-19 and beyond). The system uses a smart wearable device (wrist-worn) that monitors temperature as a trigger for second-tier screening from which a corresponding response is implemented (e.g. return to daily activity, isolate, elevated care). The continuous monitoring allows for the development of individualized thresholds (average temperature and fever threshold) rather than using population thresholds. The system identifies at-risk individuals (people with anomalies in their daily temperature profiles) for additional screening, prior to entering a facility.The most recent development at SensorComm is the partnership with the Center for Micro-Engineered Materials at the University of New Mexico, who is developing mixed-potential methane sensors using additive manufacturing. Mitigating environmental effects and financial costs of methane leaks requires a robust, widely deployable, low-cost sensing technology to provide continuous monitoring of natural gas infrastructure. Since methane emissions originate from multiple sources (agricultural or wetlands sites), sensors need to distinguish between natural gas or one of these other sources. Strategies for gathering the data, extracting the information and providing intelligence will be discussed.This talk will discuss the different techniques utilized in the data analytics and will provide key examples of how intelligence is extracted in the various applications.This material is based upon work supported by the U.S. Department of Energy, National Energy Technology Office program under Award Number DE-FOA-0031864, and partially by the National Science Foundation under Grant No. 1632498. Figure 1

Groundwater contributions of flow and nitrogen in a headwater agricultural watershed

AbstractNonpoint sources of nitrogen (N) and other nutrients are a major source of water pollution within the Chesapeake Bay watershed and other basins around the world. Human activities associated with agricultural practices can account for a large percentage of N loadings delivered to streams and rivers. This work aims to improve understanding of N transport from groundwater to surface waters, quantifying the principal hydrological processes driving water and N fluxes into and out of a headwater agricultural stream reach. The study site is a 175‐m stream reach in a heavily cultivated 40‐ha watershed in east‐central Pennsylvania. This subwatershed is underlain by fractured shale bedrock, and receives most of its baseflow from groundwater, either by diffuse matrix discharge through the streambed or by localized discharge through riparian seeps. Samples of stream, seep, and shallow groundwater were collected approximately monthly under steady hydrologic conditions in 2017. Calculated matrix flow from hydraulic head and conductivity measurements paired with differential stream gauging was used to solve for the riparian seep flux using a mass balance approach. Riparian seep fluxes ranged from 45 to 217 m3/d, transporting 0.6–4.2 kg N d−1 of nitrate‐N from the fractured bedrock aquifer to the stream. Hydrochemical data suggest that the stream is mainly disconnected from the underlying aquifer and that seeps supply essentially all water and N to the system. Seeps are likely sourced with N in nearby agricultural fields and accelerated through the system with shorter residence times than shallow groundwater. Water isotope data reinforced this notion. This study underscores the importance of agriculture as a source of N to ground and surface waters. Identifying source areas that are causing groundwater enrichment of N and seep areas where N discharges to streams is beneficial for developing N pollution mitigation strategies and implementing management practices that aim to reduce nutrient loads to the Chesapeake Bay.

The nonlinear response of fine particulate matter pollution to ammonia emission reductions in North China

Recent Chinese air pollution actions have significantly lowered the levels of fine particulate matter (PM2.5) in North China via controlling emissions of sulfur dioxide (SO2) and nitrogen oxides (NO x ) together with primary aerosols, while the emissions of another precursor, ammonia (NH3), have not yet been regulated. This raises a question that how effective the NH3 emission controls can be on the mitigation of PM2.5 pollution along with the reduction of SO2 and NO x emissions. Here we use a regional air quality model to investigate this issue focusing on the PM2.5 pollution in North China for January and July 2015. We find that the efficiency of the PM2.5 reduction is highly sensitive to the NH3 emission and its reduction intensity. Reductions in the population-weighted PM2.5 concentration (PWC) in the Beijing–Tianjin–Hebei region are only 1.4–3.8 μg m−3 (1.1%–2.9% of PM2.5) with 20%–40% NH3 emission reductions, but could reach 8.1–26.7 μg m−3 (6.2%–21%) with 60%–100% NH3 emission reductions in January 2015. Besides, the 2015–2017 emission changes (mainly reduction in SO2 emissions) could lower the PM2.5 control efficiency driven by the NH3 reduction by up to 30% for high NH3 emission conditions, while lead to no change or increase in the efficiency when NH3 emissions become low. NO x emission reductions may enhance the wintertime PM2.5 pollution due to the weakened titration effect and can be offset by simultaneously controlling NH3 emissions. Our results emphasize the need to jointly consider NH3 with SO2 and NO x emission controls when designing PM2.5 pollution mitigation strategies.

Air quality modeling to inform pollution mitigation strategies in a Latin American megacity

Poor air quality disproportionally impacts cities in low- and middle-income countries. In Bogotá, Colombia, a metropolitan area with over 10 million inhabitants, fine particulate matter (PM2.5) levels regularly exceed air quality guidelines, leading to detrimental effects on health. Although there is public interest to improve the city's air quality, the main sources of PM2.5 pollution have not been clearly identified and the use of modeling for policy development in Bogotá has been limited. Here, we apply a modeling framework based on the Community Multiscale Air Quality Modeling System (CMAQ) to conduct seasonal simulations of air pollution in Bogotá and reveal the emissions sectors with the largest contributions to PM2.5. Based on these results, we project and compare the air quality benefits of potential pollution mitigation strategies focused on these sources. The analysis finds that resuspended dust from unpaved roads is the largest local source of PM2.5 and can contribute over 30% of seasonally-averaged concentration across the city. Vehicles, industrial activity, and unpaved road dust combined are responsible for over 60% of PM2.5 pollution in Bogotá. A scenario analysis shows that paving roads can lead to PM2.5 decreases of nearly 10 μg/m3 by 2030 in some areas of the city, but air quality will deteriorate significantly over others in the absence of additional emissions control measures. Mitigation strategies designed to target the sectors with the largest contributions to PM2.5, including road cleaning systems, controls for industrial point sources, cleaner transportation fuels, and updated vehicle fleets, can largely avert projected increases in concentrations, although the impacts of different approaches vary throughout the city. This study is the first to use a comprehensive model to determine sector contributions to air pollution and inform potential emissions control policies in Bogotá, demonstrating an approach to guide pollution management in developing cities facing comparable challenges.

Association between exposure to airborne pollutants and COVID-19 in Los Angeles, United States with ensemble-based dynamic emission model

This study aims to find the association between short-term exposure to air pollutants, such as particulate matters and ground-level ozone, and SARS-CoV-2 confirmed cases. Generalized linear models (GLM), a typical choice for ecological modeling, have well-established limitations. These limitations include apriori assumptions, inability to handle multicollinearity, and considering differential effects as the fixed effect. We propose an Ensemble-based Dynamic Emission Model (EDEM) to address these limitations. EDEM is developed at the intersection of network science and ensemble learning, i.e., a specialized approach of machine learning. Generalized Additive Model (GAM), i.e., a variant of GLM, and EDEM are tested in Los Angeles and Ventura counties of California, which is one of the biggest SARS-CoV-2 clusters in the US. GAM depicts that a 1 μg/m3, 1 μg/m3, and 1 ppm increase (lag 0–7) in PM 2.5, PM 10, and O3 is associated with 4.51% (CI: 7.01 to −2.00) decrease, 1.62% (CI: 2.23 to −1.022) decrease, and 4.66% (CI: 0.85 to 8.47) increase in daily SARS-CoV-2 cases, respectively. Subsequent increment in lag resulted in the negative association between pollutants and SARS-CoV-2 cases. EDEM results in an R2 score of 90.96% and 79.16% on training and testing datasets, respectively. EDEM confirmed the negative association between particulates and SARS-CoV-2 cases; whereas, the O3 depicts a positive association; however, the positive association observed through GAM is not statistically significant. In addition, the county-level analysis of pollutant concentration interactions suggests that increased emissions from other counties positively affect SARS-CoV-2 cases in adjoining counties as well. The results reiterate the significance of uniformly adhering to air pollution mitigation strategies, especially related to ground-level ozone.

An Improved Hybrid Model on Source-Exposure Risk of Polycyclic Aromatic Hydrocarbon in Soil of Megacities

Comprehensive analysis of the source, exposure and risks of polycyclic aromatic hydrocarbons (PAHs) is very important for formulating regional soil pollution mitigation strategies. However, comprehensive information on the risks of specific sources of PAHs in regional soils is very limited. In this study, we selected the Yangtze River Delta urban agglomeration as the research area. Combined with land use types, we improved the existing source analysis receptor model, significantly increased the identification of pollution sources and improved the prediction accuracy of PAH concentrations. Urban land soil is strongly disturbed by human activities, resulting in a considerable number of pollution sources, and the PAH contribution of each pollution source is also large. In contrast, woodland and grassland soils are relatively less affected by human activities, and biomass combustion sources are dominant. By combining the ecological risk assessment and health risk assessment models of PAHs, we established an improved mixed source-risk model. An analysis using the established mixed model showed that PAHs in urban land soil caused the highest ecological risk and health risk, with risk probabilities of 56.3% and 28.2%, respectively. Moreover, the average contamination severity index (CSI) values of PAHs caused by oil combustion sources, coal combustion sources, coking furnace sources, and fuel (biomass, petroleum volatilization and diesel) combustion sources were 0.13, 0.10, 0.16 and 0.17, respectively. The average noncarcinogenic risks of PAHs caused by oil combustion sources, coal combustion sources, coking furnace sources and biomass, petroleum volatilization and diesel combustion sources to children were 0.12, 0.11, 0.08 and 0.13, respectively. Nearly half of the PAH pollution risk in forestland and grassland soil were associated with the combustion of petroleum fossil fuels, but the overall risk was low. These research results can provide support for environmental protection departments to improve regional environmental pollution management and risk prevention.

Impacts of COVID-19 on Air Quality over China: Links with Meteorological Factors and Energy Consumption

The stringent control measures in China to curb the spread of Coronavirus disease (COVID-19) have had profound societal and environmental impacts, including changes in energy consumption practices and thereby in air pollutant emissions. In this study, a suite of satellite and numerically assimilated air pollution and meteorological data combined with information on energy consumption practices and nighttime light (NTL) was used to evaluate the effects of these COVID-19 control measures on air quality. These data revealed that control measures reduced aerosols mostly over central and eastern parts of China by countering favorable meteorological conditions for increased aerosols. The control measures reduced short-lived nitrogen dioxide (NO2) with little influence on long-lived carbon monoxide (CO). Consistent with energy production and energy consumption statistics in different sectors, NTL data suggest that high human mobility within the residential sector and reduced activity in other sectors during the implementation of control measures explain small but significant decreases in black carbon and sulfate aerosols, respectively, during this period. Overall, these results provide useful information for policy makers and the scientific community by clarifying the contributions of meteorological factors and energy consumption to changes in air quality. This information can guide the development of air pollution mitigation strategies and provides insight into the air pollution status in China and the potential for long-distance transport.

Large global variations in measured airborne metal concentrations driven by anthropogenic sources

Globally consistent measurements of airborne metal concentrations in fine particulate matter (PM2.5) are important for understanding potential health impacts, prioritizing air pollution mitigation strategies, and enabling global chemical transport model development. PM2.5 filter samples (N ~ 800 from 19 locations) collected from a globally distributed surface particulate matter sampling network (SPARTAN) between January 2013 and April 2019 were analyzed for particulate mass and trace metals content. Metal concentrations exhibited pronounced spatial variation, primarily driven by anthropogenic activities. PM2.5 levels of lead, arsenic, chromium, and zinc were significantly enriched at some locations by factors of 100–3000 compared to crustal concentrations. Levels of metals in PM2.5 and PM10 exceeded health guidelines at multiple sites. For example, Dhaka and Kanpur sites exceeded the US National Ambient Air 3-month Quality Standard for lead (150 ng m−3). Kanpur, Hanoi, Beijing and Dhaka sites had annual mean arsenic concentrations that approached or exceeded the World Health Organization’s risk level for arsenic (6.6 ng m−3). The high concentrations of several potentially harmful metals in densely populated cites worldwide motivates expanded measurements and analyses.

National Clean Air Programme (NCAP) for Indian cities: Review and outlook of clean air action plans

National Clean Air Programme (NCAP), launched in 2019, is India's flagship program for better air quality in 122 cities. This review evaluates the scientific, legislative, financial, and institutional framework of the 102 publicly available clean air action plans submitted under NCAP. We assessed the robustness of the plans using the background information on pollution sources and their contribution; legal backing for the clean air action plan; cost of measures; and existing institutional accountability regime. We used the tally chart method for estimating city-specific and sector-specific mitigation measures, the number of actions under the purview of various implementation agencies, and the number of institutional, physical, and promotional interventions in the plans. Transport and road dust together cover 50% of action points, followed by interventions for the industries. Domestic cooking and heating is mentioned as a source only in 42 plans for a total of 2% of the action points. Institutional nature of the interventions was observed in 74% of the action points, using the language “overseeing, planning, proposing, preparing, investigating, identifying, ensuring, strengthening, training, studying, and engaging”. We also identified the plans that contained information on source contributions, an outline of financial requirements for executing the plans and measures for mitigating pollution from regional sources. Only 25% of the plans integrated information on the relative source contributions to formulate control strategies. Even fewer plans outlined the financial requirements for executing the plans. The institutional and administrative arrangements for ensuring inter-departmental and regional alignment in air pollution mitigation strategies are absent. We close the review with recommendations which include mandating regular updates for emission and pollution loads, granting greater fiscal autonomy for Urban Local Bodies to maintain the infrastructure necessary for sustaining air quality benefits, and moving from city-centric to airshed-centric air quality management. • Review of India's 102 publicly available city clean air action plans. • Review of the institutional, legal, and administrative arrangements for regional air quality management. • Review of availability of city-specific information on source contributions and financial commitments. • NCAP requires a central executor with legal mandate to prioritize actions and an institutional mechanism to track progress.

Exploring spatiotemporal variation characteristics of exceedance air pollution risk using social media big data

Air pollution in the form of PM2.5 decreases life expectancy considering its contribution to morbidity and mortality. Therefore, scientific and accurate PM2.5 exposure risk assessment is essential. However, the considering daily/hourly mean PM2.5 concentration and overlooking population mobility in exposure risk assessments result in underestimation of its adverse effects. Thus, using social media data and exceedance PM2.5 concentration, two novel indicators named hourly exceedance PM2.5 exposure (HEPE) and daily cumulative variation of exceedance PM2.5 exposure (DCEPE) are developed in our study. Spatiotemporal variation analysis of HEPE showed that the first exceedance PM2.5 exposure risk was observed at 10:00; this lasted till the end of the day. According to the standard deviation ellipse (SDE) method analysis results, at first, the major spatial tendency direction was northeast-southwest, with the average center (AE) located in the Yuangang Township. Then, the HEPE in the western study area increased dramatically. The major spatial tendency direction shifted from northeast-southwest to east-west. Consequently, the AE of HEPE shifted to central study area. The spatiotemporal variation characteristics led us to investigate the mechanisms. A bivariate LISA was applied to detect the spatial association between DCEPE and city functional zones (CFZs). Results showed that highly spatial associations were found between the DCEPE and CFZs in the southwest of the study area. Residential neighborhoods and transportation services showed a closer relationship with the spatial distribution of DCEPE. Based on these results, we found increasing public health threats posed by PM2.5. Thus, HEPE is an essential factor to assess air pollution exposure risk. Moreover, more attention should be paid to the spatial association between DCEPE and city functions, which is important for the development of air pollution mitigation strategies.

Using satellite remote sensing to estimate intra-urban disparities in air pollution health impacts in Washington DC

Background: Ambient air pollution in cities is of growing concern as urbanization and economic development continue, yet many cities lack information on local exposure levels and health impacts, both of which are needed to make evidence-based mitigation decisions. Remote sensing and Earth system models, though widely used for assessing air pollution health impacts globally, could be of particular value for targeted assessment of air pollution exposures and health impacts in cities where there is high population density but sparse ground-based monitoring stations.Methods: We used high resolution data inputs to assess intra-urban heterogeneity in PM2.5-attributable health impacts between 2000 and 2015, using Washington DC as a case study. Data inputs included ~1km x 1km satellite-derived global population and PM2.5 estimates; neighborhood-scale, age-standardized baseline disease rates from the DC Department of Health (DOH) and the CDC 500 Cities project; and PM2.5 concentration-response functions for chronic obstructive pulmonary disease, lung cancer, stroke and asthma emergency department visits from epidemiological meta-analyses. We also compare the spatial distribution estimated using rates obtained from the DOH versus a modeling approach used by the CDC 500 Cities projectResults: Results suggest that although PM2.5 concentrations have been decreasing in DC over time, the associated health burden continues to be unevenly distributed across the city, affecting mostly the neighborhoods with high proportions of vulnerable populations. These results support the findings from the Health Equity Report for the District of Columbia 2018, which suggests that there is a high degree of environmental and health inequality within the city.Conclusions: Urban-scale health impact assessments could help inform city-specific air pollution mitigation strategies aimed at protecting the public and reducing health disparities within the city. This case study for Washington, DC, can provide a model for estimating intra-urban air pollution-attributable health risks in other cities in the U.S. and worldwide.

Same but Different: A Framework to Design and Compare Riverbank Plastic Monitoring Strategies

Plastic pollution in rivers negatively impacts human livelihood and aquatic ecosystems. Monitoring data are crucial for a better understanding of sources, sinks and transport mechanisms of riverine macroplastics. In turn, such understanding is key to develop effective plastic pollution prevention, mitigation and removal strategies. Riverine plastic is mostly studied through the monitoring of floating plastic and through the quantification of plastic deposited on riverbanks. Existing riverbank plastic measurement methods vary greatly, which complicates direct comparison of data collected with different monitoring strategies. We present a framework to better compare and to aid the design of riverbank plastic monitoring methods, which is based on four common elements distilled from riverbank (plastic) litter monitoring methods currently in use. This framework can be used by scientists and practitioners to find the right trade-offs between the data required to answer specific research questions, and the available resources. Subsequently, we use the framework to suggest effective monitoring strategies for three frequently asked research questions. With this paper, we aim to provide a first step towards harmonization of riverbank plastic litter monitoring efforts.

Household and personal air pollution exposure measurements from 120 communities in eight countries: results from the PURE-AIR study

SummaryBackgroundApproximately 2·8 billion people are exposed to household air pollution from cooking with polluting fuels. Few monitoring studies have systematically measured health-damaging air pollutant (ie, fine particulate matter [PM2·5] and black carbon) concentrations from a wide range of cooking fuels across diverse populations. This multinational study aimed to assess the magnitude of kitchen concentrations and personal exposures to PM2·5 and black carbon in rural communities with a wide range of cooking environments.MethodsAs part of the Prospective Urban and Rural Epidemiological (PURE) cohort, the PURE-AIR study was done in 120 rural communities in eight countries (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Data were collected from 2541 households and from 998 individuals (442 men and 556 women). Gravimetric (or filter-based) 48 h kitchen and personal PM2·5 measurements were collected. Light absorbance (10−5m−1) of the PM2·5 filters, a proxy for black carbon concentrations, was calculated via an image-based reflectance method. Surveys of household characteristics and cooking patterns were collected before and after the 48 h monitoring period.FindingsMonitoring of household air pollution for the PURE-AIR study was done from June, 2017, to September, 2019. A mean PM2·5 kitchen concentration gradient emerged across primary cooking fuels: gas (45 μg/m3 [95% CI 43–48]), electricity (53 μg/m3 [47–60]), coal (68 μg/m3 [61–77]), charcoal (92 μg/m3 [58–146]), agricultural or crop waste (106 μg/m3 [91–125]), wood (109 μg/m3 [102–118]), animal dung (224 μg/m3 [197–254]), and shrubs or grass (276 μg/m3 [223–342]). Among households cooking primarily with wood, average PM2·5 concentrations varied ten-fold (range: 40–380 μg/m3). Fuel stacking was prevalent (981 [39%] of 2541 households); using wood as a primary cooking fuel with clean secondary cooking fuels (eg, gas) was associated with 50% lower PM2·5 and black carbon concentrations than using only wood as a primary cooking fuel. Similar average PM2·5 personal exposures between women (67 μg/m3 [95% CI 62–72]) and men (62 [58–67]) were observed. Nearly equivalent average personal exposure to kitchen exposure ratios were observed for PM2·5 (0·79 [95% 0·71–0·88] for men and 0·82 [0·74–0·91] for women) and black carbon (0·64 [0·45–0·92] for men and 0·68 [0·46–1·02] for women).InterpretationUsing clean primary fuels substantially lowers kitchen PM2·5 concentrations. Importantly, average kitchen and personal PM2·5 measurements for all primary fuel types exceeded WHO’s Interim Target-1 (35 μg/m3 annual average), highlighting the need for comprehensive pollution mitigation strategies.

Investigation of indoor air quality in six office buildings in Chengdu, China based on field measurements

Indoor air pollution is of a growing concern in China. The nation’s growing urban work force spends a majority of daily time in the office, thus office indoor air quality (IAQ) can be a key determinant of worker’s wellbeing. Yet, information on office IAQ in rapidly developing southwestern China remains scarce. To address this knowledge gap, we conducted an observational study based on continuous monitoring in six office buildings in Chengdu, Sichuan Province, to investigate the concentration and variability of indoor particulate matter (PM2.5) and carbon dioxide (CO2). Hourly indoor concentrations for PM2.5 and CO2 were 0–459 µg/m3 and 375–1102 ppm, respectively, with considerable intra-building and inter-building variability. Indoor PM2.5 exhibited temporal association with ambient PM2.5, while indoor CO2 exhibits both diurnal and weekly patterns. Four out of the six buildings showed a reduction in indoor PM2.5 during work hours, suggesting functional filtration systems. However, we observed daily accumulation of indoor CO2, suggesting ineffective ventilation. Indoor PM2.5 pollution could be of health concern as all buildings experienced days when indoor PM2.5 concentration was above WHO recommendation for at least 50% of the day. Multivariate linear model predicts that every 1 µg/m3 increase in ambient PM2.5 is associated with a 0.60 µg/m3 increase in indoor PM2.5. Building-specific multivariate models show work time, weekday, outdoor PM2.5 are significantly associated with indoor PM2.5 concentration. We hope findings from this study can inform future indoor pollution mitigation strategies.