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DOE OSTI2017

Chemical, biological, and DNA markers for tracing slaughterhouse effluent

Agricultural practices, if not managed correctly, can have a negative impact on receiving environments via waste disposal and discharge. In this study, a chicken slaughter facility on the rural outskirts of Sydney, Australia, has been identified as a possible source of persistent effluent discharge into a peri-urban catchment. Questions surrounding the facility's environmental management practices go back more than four decades. Despite there having never been a definitive determination of the facility's impact on local stream water quality, the New South Wales Environment Protection Authority (NSW EPA) has implemented numerous pollution reduction requirements to manage noise and water pollution at the slaughter facility. However, assessment of compliance remains complicated by potential additional sources of pollution in the catchment. To unravel this long-standing conundrum related to water pollution we apply a forensic, multiple lines of evidence approach to delineate the origin of the likely pollution source(s). Water samples collected between 2014 and 2016 from irrigation pipes and a watercourse exiting the slaughter facility had elevated concentrations of ammonia (max: 63,000 µg/L), nitrogen (max: 67,000 µg/L) and phosphorus (max: 39,000 µg/L), which were significantly higher than samples from adjacent streams that did not receive direct runoff from the facility. Arsenic, sometimes utilised in growth promoting compounds, was detected in water discharging from the facility up to ~4 times (max 3.84 µg/L) local background values (<0.5 µg/L), with inorganic As{sup (∑V+III)} being the dominant species. The spatial association of elevated water pollution to the facility could not unequivocally distinguish a source and consequently DNA analysis of a suspected pollution discharge event was undertaken. Analysis of catchment runoff from several local streams showed that only water sampled at the downstream boundary of the facility tested positive for chicken DNA, with traces of duck DNA being absent, which was a potential confounder given that wild ducks are present in the area. Further, PCR analysis showed that only the discharge water emanating from the slaughter facility tested positive for a generalized marker of anthropogenic pollution, the clinical class 1 integron-integrase gene. The environmental data collected over a three-year period demonstrates that the slaughter facility is indisputably the primary source of water-borne pollution in the catchment. Moreover, application of DNA and PCR for confirming pollution sources demonstrates its potential for application by regulators in fingerprinting pollution sources. - Highlights: • Multiple lines of evidence approach to delineate the origin of water pollution. • Arsenic speciation and clinical class 1 integron-integrase gene used as indicators. • Multidisciplinary approach for addressing global concerns about agricultural waste.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Column Aerosol Optical Properties and Aerosol Radiative Forcing During a Serious Haze-Fog Month over North China Plain in 2013 Based on Ground-Based Sunphotometer Measurements

In January 2013, North China Plain experienced several serious haze events. Cimel sunphotometer measurements at seven sites over rural, suburban and urban regions of North China Plain from 1 to 30 January 2013 were used to further our understanding of spatial-temporal variation of aerosol optical parameters and aerosol radiative forcing (ARF). It was found that Aerosol Optical Depth at 500 nm (AOD500nm) during non-pollution periods at all stations was lower than 0.30 and increased significantly to greater than 1.00 as pollution events developed. The Angstrom exponent (Alpha) was larger than 0.80 for all stations most of the time. AOD500nm averages increased from north to south during both polluted and non-polluted periods on the three urban sites in Beijing. The fine mode AOD during pollution periods is about a factor of 2.5 times larger than that during the non-pollution period at urban sites but a factor of 5.0 at suburban and rural sites. The fine mode fraction of AOD675nm was higher than 80% for all sites during January 2013. The absorption AOD675nm at rural sites was only about 0.01 during pollution periods, while 0.03-0.07 and 0.01-0.03 during pollution and non-pollution periods at other sites, respectively. Single scattering albedo varied between 0.87 and 0.95 during January 2013 over North China Plain. The size distribution showed an obvious tri-peak pattern during the most serious period. The fine mode effective radius in the pollution period was about 0.01-0.08 microns larger than during nonpollution periods, while the coarse mode radius in pollution periods was about 0.06-0.38 microns less than that during nonpollution periods. The total, fine and coarse mode particle volumes varied by about 0.06-0.34 cu microns, 0.03-0.23 cu microns, and 0.03-0.10 cu microns, respectively, throughout January 2013. During the most intense period (1-16 January), ARF at the surface exceeded −50W/sq m, −180W/sq m, and −200W/sq m at rural, suburban, and urban sites, respectively. The ARF readings at the top of the atmosphere were approximately −30W/sq m in rural and −40-60W/sq m in urban areas.

Atmosphere↗

NASA NTRS1976

International Conference on Environmental Sensing and Assessment, Las Vegas, Nev., September 14-19, 1975, Proceedings. Volumes 1 & 2

The papers deal with the detection of hazardous environmental pollutants, the development of emission control plans, and the design of compliance monitoring systems. Topics include remote sensing techniques in environmental pollution monitoring, monitoring of atmospheric particulate matter, air pollution due to sulfur dioxide and other inorganic compounds, marine pollution, atmospheric aerosols, industrial pollution, and legal aspects of pollution monitoring. Other papers examine the toxic effects of heavy metals and halogenated hydrocarbons, pollution associated with waste-disposal processes, pesticide residues in soil and groundwater, evaluations of groundwater quality, and monitoring of nuclear wastes. The interaction of climate and pollution is also discussed along with global pollutant transport, environmental modeling, ambient environmental air quality, aircraft and ground-vehicle emissions, and pollution associated with energy extraction and utilization processes. Individual items are announced in this issue.

Source record↗

DOE OSTI2024

Generating Emissions Inventory for Carbon Capture and Storage Analysis for Carbon-Intensive Industrial Sectors

Decarbonizing the industrial sector is critical to achieve carbon dioxide (CO2) emissions reductions goals of the Biden Administration. Currently available decarbonization options include electrification, fuel switching to zero carbon fuels like green hydrogen (H2) and carbon capture and storage (CCS). Application of post-combustion carbon capture (PCCC) technology in the power sector, as well as research at the U.S. Department of Energy's Fossil Energy and Carbon Management (FECM) Office has shown that its application in the industrial sector could have co-benefits in the form of emissions reductions of non-CO2 regulated pollutants. For example, solvent based PCCC systems require pre-conditioning of flue gas to remove sulfur and particulate matter (PM) upstream of the CO2 absorber. However, there is a lack of understanding about the type of non-CO2 pollutants which can be captured and the amount of reduction possible. PCCC application differs across industrial sectors as it depends on the availability of decarbonization options, characteristics of industrial processes and the amount and composition of pollutant flows. Certain facilities can also have multiple effluent flows with or without a CO2 stream. As such, understanding industrial processes and their effluent flows in detail is required to quantify the co-benefits opportunities presented by PCCC. Considering this requirement, the goal of this analysis is to develop a high-resolution inventory of effluent flows from facilities of 8 industrial sectors in the U.S. These industrial sectors - ethanol, ammonia, cement, steel, natural gas processing, hydrogen, petroleum refining and wood and pulp products - have carbon-intensive effluent flows, and thus are prime candidates for PCCC applications. In this study, we map the composition of pollutant flow from flue stacks across the identified facilities. Using data available in three Environmental Protection Agency (EPA) databases - the Green House Gas Reporting Program (GHGRP), the National Emissions Inventory (NEI) and the Toxic Release Inventory (TRI), we create a combined inventory which lists the type, amount, and concentration of pollutant flows. Using total weight of the pollutant flow back calculated from observed data for CO2 concentrations in flue gas for individual sectors, we calculate the concentration of each pollutant in the flue gas stream. Thus, the resultant emissions inventory includes the following details for each facility in the sector: facility-level and if possible, process-level pollutant flows, concentrations of pollutants in the flue gas, and geographical coordinates of the facilities. A detailed statistical analysis and summary allows us to search for erroneous data and remove them from the final inventory. The generation of the inventory is achieved using a python-based framework which can recreate this inventory for other industrial sectors as well as using newer releases of emission inventories from EPA. The statistical analysis performed on the inventory is also calibrated and automated to identify outliers efficiently.

air pollutants↗

DOE OSTI2016

A multi-model assessment of the co-benefits of climate mitigation for global air quality

The recent International Panel on Climate change (IPCC) report identifies significant co-benefits from climate policies on near-term ambient air pollution and related human health outcomes [1]. This is increasingly relevant for policy making as the health impacts of air pollution are a major global concern- the Global Burden of Disease (GBD) study identifies outdoor air pollution as the sixth major cause of death globally [2]. Integrated assessment models (IAMs) are an effective tool to evaluate future air pollution outcomes across a wide range of assumptions on socio-economic development and policy regimes. The Representative Concentration Pathways (RCPs) [3] were the first set of long-term global scenarios developed across multiple integrated assessment models that provided detailed estimates of a number of air pollutants until 2100. However these scenarios were primarily designed to cover a defined range of radiative forcing outcomes and thus did not specifically focus on the interactions of long-term climate goals on near-term air pollution impacts. More recently, [4] used the RCP4.5 scenario to evaluate the co-benefits of global GHG reductions on air quality and human health in 2030. [5-7] have further examined the interactions of more diverse pollution control regimes with climate policies. This paper extends the listed studies in a number of ways. Firstly it uses multiple IAMs to look into the co-benefits of a global climate policy for ambient air pollution under harmonized assumptions on near-term air pollution control. Multi-model frameworks have been extensively used in the analysis of climate change mitigation pathways, and the structural uncertainties regarding the underlying mechanisms (see for example [8-10]. This is to our knowledge the first time that a multi-model evaluation has been specifically designed and applied to analyze the co-benefits of climate change policy on ambient air quality, thus enabling a better understanding of at a detailed sector and region level. A second methodological advancement is a quantification of the co-benefits in terms of the associated atmospheric concentrations of fine particulate matter (PM2.5) and consequent mortality related outcomes across different models. This is made possible by the use of state-of the art simplified atmospheric model that allows for the first time a computationally feasible multi-model evaluation of such outcomes.

Rao, Shilpa↗

DOE OSTI2022

Quantifying Impacts of Renewable Electricity Deployment on Air Quality and Human Health in Southeast Asia Based on Aims III Scenarios

Exposure to outdoor air pollution is the largest environmental risk factor for death and disease worldwide, associated with millions of cases of excess deaths (mortality) each year. Although there are many pollutants in the air that affect our health, the most important class of pollutants is fine particulate matter, PM 2.5 , which are airborne particles of diameter ≤2.5 micrometers (µm). These particles are small enough to deposit deep in the respiratory system where they can then enter the bloodstream, traveling and causing damage to other bodily systems. Exposure to outdoor (ambient) PM 2.5 has been found to be the most important environmental risk factor for mortality in Southeast Asia, associated with 130,000 - 320,000 excess deaths in Association of Southeast Asian Nations (ASEAN) member countries in 2019. Southeast Asia, especially its mega-cities, but also other areas, has some of the worst air quality in the world. Almost all human activity emits air pollutants. Fine particulate matter is both directly emitted and formed in the atmosphere through chemical reactions, the latter of which requires modeling to predict. Power generation is one of the major sources of air pollutants that lead to elevated concentrations of fine particulate matter, including in Southeast Asia. Fossil fuel combustion for power generation, especially coal but also diesel, is the main source of air pollutant emissions from power generation. While natural gas burns cleaner than coal or diesel, in the quantities combusted for power generation in Southeast Asia, it is also a significant emitter. This study augments the ASEAN Interconnection Masterplan Study III (AIMS III) by quantifying changes to air quality and human health that result from its renewable integration and transmission interconnection scenarios. Performing this analysis requires translation of the changes in projected generation from different power sector fuel sources in the AIMS III scenarios to changes in air pollutant emissions, developing what's known as an emissions inventory for each scenario and year evaluated. We then use for the first time a new global, reduced-complexity air quality model to transform the changes in emissions to changes in air pollutant concentration of the deadliest air pollutant for human health, fine particulate matter (or PM 2.5 ). The air quality model, Global InMAP, then utilizes the location of human population in ASEAN countries to calculate exposure to PM 2.5 concentration changes and translates that to estimates of excess mortality attributable to the AIMS III scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Cyclist exposure to black carbon, ultrafine particles and heavy metals: An experimental study along two commuting routes near Antwerp, Belgium

Highlights: • Wearable UFP and BC instrumentation was carried along whilst cycling 2 different commuting routes. • HR-ICP-MS provided elemental information on railway- and roadside-exposed routes. • Heart rate was tracked simultaneously to estimate the personal inhaled pollutant dose. • The bicycle highway route exhibited 300% lower BC and 20% lower UFP concentrations. • Elemental differences are observed between the considered commuting routes. Urban environments typically exhibit large atmospheric pollution variation, in both space and time. In contrast to traditional monitoring networks suffering from a limited spatial coverage, mobile platforms enable personalized high-resolution monitoring, providing valuable insights into personal atmospheric pollution exposure, and the identification of potential pollution hotspots. This study evaluated personal cyclist exposure to UFPs, BC and heavy metals whilst commuting near Antwerp, Belgium, by performing mobile measurements with wearable black carbon (BC) and ultrafine particle (UFP) instruments. Loaded micro-aethalometer filterstrips were chemically analysed and the inhaled pollutant dose determined from the exhibited heart rate. Considerable spatial pollutant variation was observed along the travelled routes, with distinct contributions from spatial factors (e.g. traffic intersections, urban park and market) and temporary events. On average 300% higher BC, 20% higher UFP and changing elemental concentrations are observed along the road traffic route (RT), when compared to the bicycle highway route (BH). Although the overall background pollution determines a large portion of the experienced personal exposure (in this case 53% for BC and 40% for UFP), cyclists can influence their personal atmospheric pollution exposure, by selecting less exposed commuting routes. Our results, hereby, strengthen the body of evidence in favour of further policy investments in isolated bicycle infrastructure.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2013

Toward the Next Generation of Air Quality Monitoring Indicators

This paper introduces an initiative to bridge the state of scientific knowledge on air pollution with the needs of policymakers and stakeholders to design the "next generation" of air quality indicators. As a first step this initiative assesses current monitoring and modeling associated with a number of important pollutants with an eye toward identifying knowledge gaps and scientific needs that are a barrier to reducing air pollution impacts on human and ecosystem health across the globe. Four outdoor air pollutants were considered e particulate matter, ozone, mercury, and Persistent Organic Pollutants (POPs) e because of their clear adverse impacts on human and ecosystem health and because of the availability of baseline data for assessment for each. While other papers appearing in this issue will address each pollutant separately, this paper serves as a summary of the initiative and presents recommendations for needed investments to provide improved measurement, monitoring, and modeling data for policyrelevant indicators. The ultimate goal of this effort is to enable enhanced public policy responses to air pollution by linking improved data and measurement methods to decision-making through the development of indicators that can allow policymakers to better understand the impacts of air pollution and, along with source attribution based on modeling and measurements, facilitate improved policies to solve it. The development of indicators represents a crucial next step in this process.

Particulate matter↗

DOE OSTI2019

Evaluation of China’s Environmental Pressures Based on Satellite NO 2 Observation and the Extended STIRPAT Model

China’s rapid urbanization and industrialization have affected the spatiotemporal patterns of nitrogen dioxide (NO 2 ) pollution, which has caused greater environmental pressures. In order to mitigate the environmental pressures because of NO 2 pollution, it is of vital importance to investigate the influencing factors. We first obtained data for NO 2 pollution at the city level using satellite observation techniques and analyzed its spatial distribution. Next, we introduced a theoretical framework, an extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, to quantify the relationship between NO 2 pollution and its contributing natural and socio-economic factors. The results are as follows. Cities with high NO 2 pollution are mainly concentrated in the North China Plain. On the contrary, southwestern cities are characterized by low NO 2 pollution. In addition, we find that population, per capita gross domestic product, the share of the secondary industry, ambient air pressures, total nighttime light data, and urban road area have a positive impact on NO 2 pollution. In contrast, increases in the normalized difference vegetation index (NDVI), relative humidity, temperature, and wind speed may reduce NO 2 pollution. These empirical results should help the government to effectively and efficiently implement further emission reductions and energy saving policies in Chinese cities in a bid to mitigate the environmental pressures.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Recent intensification of winter haze in China linked to foreign emissions and meteorology

Wintertime aerosol pollution in Northern China has increased over the past several decades as anthropogenic emissions in China have increased, and has increased dramatically since the beginning of the 21st century, but the causes and their quantitative contributions remain uncertain. Here we use an aerosol source tagging capability implemented in a global aerosol-climate model to assess long-term trends of PM2.5 (particulate matter less than 2.5 µm in diameter) in Northern China. Our analysis suggests that increasing PM2.5 concentrations due to local emission increases within China were obscured (~13%) by foreign emission reductions between 1980–2000. As foreign emissions stabilized during 2000-2014, their counteracting effect almost disappeared, uncovering China’s pollution potential from domestic emission increases. The meteorology dominated PM2.5 trend during 1990–1996 and also uncovered the pollution potential due to decadal variations in winds. The stabilized foreign emissions together with changing meteorology explain a quarter of the larger increasing trend of PM2.5 since the beginning of the 21st century. Future foreign emissions are not expected to help hiding China’s pollution, reductions in local emissions are the efficient way to improve future air quality in Northern China.

Yang, Yang↗

DOE OSTI2023

Geochemistry and metagenomics analyses of bacterial community structure in selected waste dumpsites in Lagos Metropolis, Nigeria

Dumpsites are reservoirs of persistent organic pollutants (POPs) and heavy metals (HMs), constituting environmental hazards to humanity. Autochthonous microorganisms in dumpsites exhibit various degrees of responses to contaminants. Unfortunately, there is a dearth of information on the types and concentration of pollutants and the array of microorganisms in these dumpsites which may play important roles in the metabolism of such pollutants or other community processes. Therefore, determining the microbial community structure in such contaminated sites across a municipality is essential for profiling the taxa that would serve as consensus degraders of the pollutants. In this study, soil samples from three dumpsites (Cele, CS; Solous, SS; and Computer Village, CVS) were characterized for geochemical properties using GC-MS, MP-AES, and other analytical protocols, while the dynamics of bacterial communities were evaluated based on their 16S rRNA gene barcodes. A significant difference in the bacterial communities was observed among the dumpsites in relation to the extent of pollution caused by POPs and HMs. CVS, with the highest HM contamination, was rich in Actinobacteria (41.7%) and Acidobacteria (10.2%), in contrast to CS and SS. Proteobacteria (34.1%) and Firmicutes (20%) were the dominant phyla in CS (highest POP contamination), while Bacteroidetes (45.5%) and Proteobacteria (39.9%) were dominant in SS soil.Bacilluswas the dominant genus in the most polluted dumpsite. Canonical correspondence analysis revealed that polycyclic aromatic hydrocarbons (PAHs) and HMs shaped the structure of the bacterial operational taxonomic units (OTUs) in the most polluted dumpsite. Out of a total of 706 OTUs, 628 OTUs exhibited a significant correlation (>50%) with benzo(b)fluoranthene, azobenzene, dibenzofurans, pyrene, dibenzo(a,l)pyrene, Cu, and Zn. In particular, Proteobacteria (Achromobactersp. andSerratiasp.), Bacteroidetes (Zhouiasp.), and Firmicutes (Bacillussp.) were suggested to be pivotal to the ecophysiology of dumpsite soils contaminated with POPs and HMs. The results generally underscored the importance of metagenomic and physicochemical analyses of polluted systems in enabling correlations for useful prediction of drivers of such ecosystems. This will further improve our understanding of the metabolic potential and adaptation of organisms in such systems.

Environmental Sciences & Ecology↗

DOE OSTI2017

Sensitivity to Thermonuclear Reaction Rates in Modeling the Abundance Anomalies of NGC 2419

Abundance anomalies in globular clusters provide strong evidence for multiple stellar populations within each cluster. These populations are usually interpreted as distinct generations, with the currently observed second-generation stars having formed in part from the ejecta of massive, first-generation “polluter” stars, giving rise to the anomalous abundance patterns. The precise nature of the polluters and their enrichment mechanism are still unclear. Even so, the chemical abundances measured in second-generation stars within the globular cluster NGC 2419 provide insight into this puzzling process. Previous work used Monte Carlo nuclear reaction network calculations to constrain the temperature–density conditions that could reproduce the observed abundances, thereby placing robust limits on the origins of the polluter material. The effect of individual reaction rates on these conditions has not been studied, however. Thus, we perform an exhaustive sensitivity study on the nuclear physics input to determine which reactions have the greatest impact on these predictions. We find that the {sup 30}Si(p,γ){sup 31}P, {sup 37}Ar(p,γ){sup 38}K, {sup 38}Ar(p,γ){sup 39}K, and {sup 39}K(p,γ){sup 40}Ca reactions are all critical in determining the temperature–density conditions, and ultimately, the origins of the polluter material. We conclude with recommendations for future experiments.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Automated identification of local contamination in remote atmospheric composition time series

Abstract. Atmospheric observations in remote locations offer a possibility of exploring trace gas and particle concentrations in pristine environments. However, data from remote areas are often contaminated by pollution from local sources. Detecting this contamination is thus a central and frequently encountered issue. Consequently, many different methods exist today to identify local contamination in atmospheric composition measurement time series, but no single method has been widely accepted. In this study, we present a new method to identify primary pollution in remote atmospheric datasets, e.g., from ship campaigns or stations with a low background signal compared to the contaminated signal. The pollution detection algorithm (PDA) identifies and flags periods of polluted data in five steps. The first and most important step identifies polluted periods based on the derivative (time derivative) of a concentration over time. If this derivative exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median, and a sparse data filter (optional). The PDA only relies on the target dataset itself and is independent of ancillary datasets such as meteorological variables. All parameters of each step are adjustable so that the PDA can be “tuned” to be more or less stringent (e.g., flag more or fewer data points as contaminated). The PDA was developed and tested with a particle number concentration dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic. Using strict settings, we identified 62 % of the data as influenced by local contamination. Using a second independent particle number concentration dataset also collected during MOSAiC, we evaluated the performance of the PDA against the same dataset cleaned by visual inspection. The two methods agreed in 94 % of the cases. Additionally, the PDA was successfully applied to a trace gas dataset (CO2), also collected during MOSAiC, and to another particle number concentration dataset, collected at the high-altitude background station Jungfraujoch, Switzerland. Thus, the PDA proves to be a useful and flexible tool to identify periods affected by local contamination in atmospheric composition datasets without the need for ancillary measurements. It is best applied to data representing primary pollution. The user-friendly and open-access code enables reproducible application to a wide suite of different datasets. It is available at https://doi.org/10.5281/zenodo.5761101 (Beck et al., 2021).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

Limitations in Representation of Physical Processes Prevents Successful Simulation of PM2.5 During KORUS-AQ

High levels of fine particulate matter (PM2.5) pollution in East Asia often exceed local air quality standards. Observations from the Korea United States-Air Quality (KORUS-AQ) field campaign in May and June 2016 showed that development of extreme pollution (haze) occurred through a combination of long-range transport and favorable meteorological conditions that enhanced local production of PM2.5. Atmospheric models often have difficulty simulating PM2.5 chemical composition during haze, which is of concern for the development of successful control measures. We use observations from KORUS-AQ to examine the ability of the GEOS-Chem chemical transport model to simulate PM2.5 composition throughout the campaign and identify the mechanisms driving the pollution event. At the surface, the model underestimates sulfate by -64% but overestimates nitrate by +36%. The largest underestimate in sulfate occurs during the pollution event, where models typically struggle to generate elevated sulfate concentrations due to missing heterogeneous chemistry in aerosol liquid water in the polluted boundary layer. Hourly surface observations show that the model nitrate bias is driven by an overestimation of the nighttime peak. In the model, nitrate formation is limited by the supply of nitric acid, which is biased by +100% against aircraft observations. We hypothesize that this is due to a large missing sink, which we implement here as a factor of five increase in dry deposition. We show that the resulting increased deposition velocity is consistent with observations of total nitrate as a function of photochemical age. The model does not account for factors such as the urban heat island effect or the heterogeneity of the built-up urban landscape resulting in insufficient model turbulence and surface area over the study area that likely results in insufficient dry deposition. Other species such as NH3 could be similarly affected but were not measured during the campaign. Nighttime production of nitrate is driven by NO2 hydrolysis in the model, while observations show that unexpectedly elevated nighttime ozone (not present in the model) should result in N2O5 hydrolysis as the primary pathway. The model is unable to represent nighttime ozone due to an overly rapid collapse of the afternoon mixed layer and excessive titration by NO. We attribute this to missing nighttime heating driving deeper nocturnal mixing that would be expected to occur in a city like Seoul. This urban heating is not considered in air quality models run at large enough scales to treat both local chemistry and long-range transport. Key model failures in simulating nitrate, mainly overestimated daytime nitric acid, incorrect representation of nighttime chemistry, and an overly shallow and insufficiently turbulent nighttime mixed layer, exacerbate the model’s inability to simulate the buildup of PM2.5 during haze pollution. To address the underestimate in sulfate most evident during the haze event, heterogeneous aerosol uptake of SO2 is added to the model which previously only considered aqueous production of sulfate from SO2 in cloud water. Implementing a simple parameterization of this chemistry improves the model abundance of sulfate but degrades the SO2 simulation implying that emissions are underestimated. We find that improving model simulations of sulfate has direct relevance to determining local vs. transboundary contributions to PM2.5. During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO2 decreases the fraction of PM2.5 attributable to long-range transport from 66% to 54%. Locally-produced sulfate increased from 1% to 25% of locally-produced PM2.5, implying that local emissions controls would have a larger effect than previously thought. However, this additional uptake of SO2 is coupled to the model nitrate prediction which affects the aerosol liquid water abundance and chemistry driving sulfate-nitrate-ammonium partitioning. An additional simulation of the haze pollution with heterogeneous uptake of SO2 to aerosol and simple improvements to the model nitrate simulation results in 30% less sulfate due to 40% less nitrate and aerosol water, and results in an underestimate of sulfate during the haze event. Future studies need to better consider the impact of model physical processes such as dry deposition and nighttime boundary layer mixing on the simulation of nitrate and the effect of improved nitrate simulations on the overall simulation of secondary inorganic aerosol (sulfate+nitrate+ammonium) in East Asia. Foreign emissions are rapidly changing, increasing the need to understand the impact of local emissions on PM2.5 in South Korea to ensure continued air quality improvements.

Katherine R. Travis↗

NASA NTRS2021

Communicating Respiratory Health Risk Among Children using a Global Air Quality Index

Air pollution poses a serious threat to children’s respiratory health around the world. Satellite remote-sensing technology and air quality models can provide pollution data on a global scale, necessary for riskcommunication efforts in regions without ground-based monitoring networks. Several large centers, including NASA, produce global pollution forecasts that may be used alongside air quality indices to communicate local, daily risk information to the public. Here we present a health-based, globally applicable air quality index developed specifically to reflect the respiratory health risks among children exposed to elevated outdoor air pollution. Additive, excess-risk air quality indices were developed using 51 different coefficients derived from time-series health studies evaluating the impacts of ambient fine particulate matter, nitrogen dioxide, and ozone on children's respiratory morbidity outcomes. A total of four indices were created which varied based on whether or not the underlying studies controlled for co-pollutants and in the adjustment of excess risks of individual pollutants. Combined with historical estimates of air pollution provided globally at a 25x25 km2 spatial resolution from the NASA's Goddard Earth Observing System composition forecast (GEOS-CF) model, each of these indices were examined in a global sample of 664 small and 140 large cities for study year 2017. Adjusted indices presented the most normal distributions of locally-scaled index values, which has been shown to improve associations with health risks, while indices based on coefficients controlling for co-pollutants had little effect on index performance. We provide the steps and resources need to apply our final adjusted index at the local level using freely-available forecasting data from the GEOS-CF model, which can provide risk communication information for cities around the world to better inform individual behavior modification to best protect children's respiratory health.

Air Qualilty↗

DOE OSTI2024

The value of adding black carbon to community monitoring of particulate matter

Low-cost particulate matter (PM) sensors are increasingly used by researchers, public health agencies, and the public to measure spatial and temporal variations in air pollution, which can inform strategies for community air pollution reduction. While low-cost PM sensors provide a valuable measure of harmful fine particulate matter (PM 2.5 ), a significant portion of ambient PM 2.5 is typically the secondary product of air pollution emitted by varied sources outside of community boundaries. In contrast, concentrations of black carbon (BC), a component of PM 2.5 , are directly emitted by a few specific sources, such as diesel engines within communities. Motivated by community organizations seeking to understand persistent sources of local pollution, this study deployed a suite of custom-built BC sensors alongside a network of low-cost PM sensors for four weeks in two seasons at 50 stationary locations in the adjacent cities of Richmond, North Richmond, and San Pablo, California, east of the San Francisco Bay. Concentrations of BC varied more than PM 2.5 both temporally and spatially. Monthly network-average BC was 3×higher in winter than late spring, while PM 2.5 was only 10% lower. In both seasons, average PM 2.5 concentrations at two-thirds of sites were within ±10% of the network average, whereas two-thirds of sites had BC levels outside of ±10% of the network-average concentration. The most and least polluted locations were more persistent across seasons for BC than PM 2.5 , and the temporal dynamics of BC at these sites were similar, signifying that they are impacted by the same emission sources. Together, these spatiotemporal trends show that BC is a better indicator of the proximity and activity of local pollution sources than PM 2.5 . Thus, including BC in addition to PM 2.5 in community monitoring networks can provide additional insights about local sources of air pollution.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

A cross-disciplinary evaluation of evidence for multipollutant effects on cardiovascular disease

Highlights: • Efficiencies may be gained using a multipollutant context to regulate air pollutants. • We perform an evaluation of multipollutant effects on cardiovascular disease CVD. • effect of combined exposure than either PM or O{sub 3} exposure alone. • Heterogeneous results were observed across disciplines for other two-pollutant combinations: PM and CO, NO{sub 2} and PM{sub 10}, O{sub 3} and NO{sub 2}, CO and O{sub 3}. The current single-pollutant approach to regulating ambient air pollutants is effective at protecting public health, but efficiencies may be gained by addressing issues in a multipollutant context since multiple pollutants often have common sources and individuals are exposed to more than one pollutant at a time.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Benzene homologues contaminants in a former herbicide factory site: distribution, attenuation, risk, and remediation implication

Benzene homologues often used as organic raw materials or as detergents in chemical industry are prone to accidental release into the environment which can cause serious long-term soil pollutions. In a large former herbicide factory site, we investigated 43 locations for benzene homologues contaminations in soil, soil gas, and groundwater; and studied the hydrogeological conditions. An inverse distance weighted interpolation method was employed to determine the pollutants three-dimensional spatial distribution in the soils. Results showed that benzene homologues residues were mainly originated from the herbicide production workshop; and that the pollution had horizontally expanded at the deeper soil layer. Contaminants had already migrated 15m downward from ground surface. Contaminant phase distribution study showed that NAPL was the primary phase (>99%) for the pollutants accumulated in the unsaturated zone; while it had not migrated to groundwater. The primary mechanism for contaminant transport and attenuation included dissolution of “occluded” NAPL into pore water and pollutant volatilization into soil pore space. Risk assessment revealed that the pollutants brought unacceptable high carcinogenic and non-carcinogenic risks to public health. In order to convert this former chemical processing factory site into a residential area, a remediation to the polluted production workshop sites is urgently required.

Yang, Shuo↗