Mapping air quality zones for coastal urban centers

This study presents a new method that incorporates modern air dispersion models allowing local terrain and land–sea breeze effects to be considered along with political and natural boundaries for more accurate mapping of air quality zones (AQZs) for coastal urban centers. This method uses local coastal wind patterns and key urban air pollution sources in each zone to more accurately calculate air pollutant concentration statistics. The new approach distributes virtual air pollution sources within each small grid cell of an area of interest and analyzes a puff dispersion model for a full year’s worth of 1-hr prognostic weather data. The difference of wind patterns in coastal and inland areas creates significantly different skewness (S) and kurtosis (K) statistics for the annually averaged pollutant concentrations at ground level receptor points for each grid cell. Plotting the S-K data highlights grouping of sources predominantly impacted by coastal winds versus inland winds. The application of the new method is demonstrated through a case study for the nation of Kuwait by developing new AQZs to support local air management programs. The zone boundaries established by the S-K method were validated by comparing MM5 and WRF prognostic meteorological weather data used in the air dispersion modeling, a support vector machine classifier was trained to compare results with the graphical classification method, and final zones were compared with data collected from Earth observation satellites to confirm locations of high-exposure-risk areas. The resulting AQZs are more accurate and support efficient management strategies for air quality compliance targets effected by local coastal microclimates.

Implications: A novel method to determine air quality zones in coastal urban areas is introduced using skewness (S) and kurtosis (K) statistics calculated from grid concentrations results of air dispersion models. The method identifies land–sea breeze effects that can be used to manage local air quality in areas of similar microclimates.     

Forecasting air quality time series using deep learning

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O₃) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O₃ exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours.

Implications: Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.     

Application of an integrated Weather Research and Forecasting (WRF)/CALPUFF modeling tool for source apportionment of atmospheric pollutants for air quality management: A case study in the urban area of Benxi,China

In this study, the authors endeavored to develop an effective framework for improving local urban air quality on meso-micro scales in cities in China that are experiencing rapid urbanization. Within this framework, the integrated Weather Research and Forecasting (WRF)/CALPUFF modeling system was applied to simulate the concentration distributions of typical pollutants (particulate matter with an aerodynamic diameter <10 μm [PM₁₀], sulfur dioxide [SO₂], and nitrogen oxides [NO_x]) in the urban area of Benxi. Statistical analyses were performed to verify the credibility of this simulation, including the meteorological fields and concentration fields. The sources were then categorized using two different classification methods (the district-based and type-based methods), and the contributions to the pollutant concentrations from each source category were computed to provide a basis for appropriate control measures. The statistical indexes showed that CALMET had sufficient ability to predict the meteorological conditions, such as the wind fields and temperatures, which provided meteorological data for the subsequent CALPUFF run. The simulated concentrations from CALPUFF showed considerable agreement with the observed values but were generally underestimated. The spatial-temporal concentration pattern revealed that the maximum concentrations tended to appear in the urban centers and during the winter. In terms of their contributions to pollutant concentrations, the districts of Xihu, Pingshan, and Mingshan all affected the urban air quality to different degrees. According to the type-based classification, which categorized the pollution sources as belonging to the Bengang Group, large point sources, small point sources, and area sources, the source apportionment showed that the Bengang Group, the large point sources, and the area sources had considerable impacts on urban air quality. Finally, combined with the industrial characteristics, detailed control measures were proposed with which local policy makers could improve the urban air quality in Benxi. In summary, the results of this study showed that this framework has credibility for effectively improving urban air quality, based on the source apportionment of atmospheric pollutants.

Implications: The authors endeavored to build up an effective framework based on the integrated WRF/CALPUFF to improve the air quality in many cities on meso-micro scales in China. Via this framework, the integrated modeling tool is accurately used to study the characteristics of meteorological fields, concentration fields, and source apportionments of pollutants in target area. The impacts of classified sources on air quality together with the industrial characteristics can provide more effective control measures for improving air quality.

Through the case study, the technical framework developed in this study, particularly the source apportionment, could provide important data and technical support for policy makers to assess air pollution on the scale of a city in China or even the world.     

Air quality measurements—From rubber bands to tapping the rainbow

It is axiomatic that good measurements are integral to good public policy for environmental protection. The generalized term for “measurements” includes sampling and quantitation, data integrity, documentation, network design, sponsorship, operations, archiving, and accessing for applications. Each of these components has evolved and advanced over the last 200 years as knowledge of atmospheric chemistry and physics has matured. Air quality was first detected by what people could see and smell in contaminated air. Gaseous pollutants were found to react with certain materials or chemicals, changing the color of dissolved reagents such that their light absorption at selected wavelengths could be related to both the pollutant chemistry and its concentration. Airborne particles have challenged the development of a variety of sensory devices and laboratory assays for characterization of their enormous range of physical and chemical properties. Advanced electronics made possible the sampling, concentration, and detection of gases and particles, both in situ and in laboratory analysis of collected samples. Accurate and precise measurements by these methods have made possible advanced air quality management practices that led to decreasing concentrations over time. New technologies are leading to smaller and cheaper measurement systems that can further expand and enhance current air pollution monitoring networks.

Implications: Ambient air quality measurement systems have a large influence on air quality management by determining compliance, tracking trends, elucidating pollutant transport and transformation, and relating concentrations to adverse effects. These systems consist of more than just instrumentation, and involve extensive support efforts for siting, maintenance, calibration, auditing, data validation, data management and access, and data interpretation. These requirements have largely been attained for criteria pollutants regulated by National Ambient Air Quality Standards, but they are rarely attained for nonroutine measurements and research studies.     

Long-term trends in airborne SO2 in an air quality monitoring station in Seoul,Korea, from 1987 to 2013

Atmospheric concentration of sulfur dioxide (SO₂) was intermittently measured at an air quality monitoring (AQM) station in the Yong-san district of Seoul, Korea, between 1987 and 2013. The SO₂ level was compared with other important pollutants concurrently measured, including methane (CH₄), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO₂), ozone (O₃), and particulate matter (PM₁₀). If split into three different periods (period 1, 1987–1988, period 2, 1999–2000, and period 3, 2004–2013), the respective mean [SO₂] values (6.57 ± 4.29, 6.30 ± 2.44, and 5.29 ± 0.63 ppb) showed a slight reduction across the entire study period. The concentrations of SO₂ are found to be strongly correlated with other pollutants such as CO (r = 0.614, p = 0.02), which tracked reductions in reported emissions due to tighter emissions standards enacted by the South Korean government. There was also a clear seasonal trend in the SO₂ level, especially in periods 2 and 3, reflecting the combined effects of domestic heating by coal briquettes and meteorological conditions. Although only a 16% concentration reduction was achieved during the 27-year study duration, this is significant if one considers rapid urbanization, an 83.2% increase in population, and rapid industrialization that took place during that period.

Implications: Since 1970, a network of air quality monitoring (AQM) stations has been operated by the Korean Ministry of Environment (KMOE) for routine nationwide monitoring of air pollutant concentrations in urban/suburban areas. To date, the information obtained from these stations has provided a platform for analyzing long-term trends of major pollutant species. In this study, we examined the long-term trends of SO₂ levels and relevant environmental parameters monitored continuously in the Yong-san district of Seoul between 1987 and 2013. The data were analyzed over various time scales (i.e., monthly, seasonal, and annual intervals). The results obtained from this study will allow us to assess the effectiveness of abatement strategy and to predict future concentrations trends in association with future abatement strategies and technologies.     

Updated methods for assessing the impacts of nearby gas drilling and production on neighborhood air quality and human health

An explosive growth in natural gas production within the last decade has fueled concern over the public health impacts of air pollutant emissions from oil and gas sites in the Barnett and Eagle Ford shale regions of Texas. Commonly acknowledged sources of uncertainty are the lack of sustained monitoring of ambient concentrations of pollutants associated with gas mining, poor quantification of their emissions, and inability to correlate health symptoms with specific emission events. These uncertainties are best addressed not by conventional monitoring and modeling technology, but by increasingly available advanced techniques for real-time mobile monitoring, microscale modeling and source attribution, and real-time broadcasting of air quality and human health data over the World Wide Web. The combination of contemporary scientific and social media approaches can be used to develop a strategy to detect and quantify emission events from oil and gas facilities, alert nearby residents of these events, and collect associated human health data, all in real time or near-real time. The various technical elements of this strategy are demonstrated based on the results of past, current, and planned future monitoring studies in the Barnett and Eagle Ford shale regions.

Implications: Resources should not be invested in expanding the conventional air quality monitoring network in the vicinity of oil and gas exploration and production sites. Rather, more contemporary monitoring and data analysis techniques should take the place of older methods to better protect the health of nearby residents and maintain the integrity of the surrounding environment.     

Evaluating the effectiveness of air quality regulations: A review of accountability studies and frameworks

Assessments of past environmental policies—termed accountability studies—contribute important information to the decision-making process used to review the efficacy of past policies, and subsequently aid in the development of effective new policies. These studies have used a variety of methods that have achieved varying levels of success at linking improvements in air quality and/or health to regulations. The Health Effects Institute defines the air pollution accountability framework as a chain of events that includes the regulation of interest, air quality, exposure/dose, and health outcomes, and suggests that accountability research should address impacts for each of these linkages. Early accountability studies investigated short-term, local regulatory actions (for example, coal use banned city-wide on a specific date or traffic pattern changes made for Olympic Games). Recent studies assessed regulations implemented over longer time and larger spatial scales. Studies on broader scales require accountability research methods that account for effects of confounding factors that increase over time and space. Improved estimates of appropriate baseline levels (sometimes termed “counterfactual”—the expected state in a scenario without an intervention) that account for confounders and uncertainties at each link in the accountability chain will help estimate causality with greater certainty. In the direct accountability framework, researchers link outcomes with regulations using statistical methods that bypass the link-by-link approach of classical accountability. Direct accountability results and methods complement the classical approach. New studies should take advantage of advanced planning for accountability studies, new data sources (such as satellite measurements), and new statistical methods. Evaluation of new methods and data sources is necessary to improve investigations of long-term regulations, and associated uncertainty should be accounted for at each link to provide a confidence estimate of air quality regulation effectiveness. The final step in any accountability is the comparison of results with the proposed benefits of an air quality policy.

Implications: The field of air pollution accountability continues to grow in importance to a number of stakeholders. Two frameworks, the classical accountability chain and direct accountability, have been used to estimate impacts of regulatory actions, and both require careful attention to confounders and uncertainties. Researchers should continue to develop and evaluate both methods as they investigate current and future air pollution regulations.     

Long-term associations of morbidity with air pollution: A catalog and synthesis

I searched the National Institutes of Health MEDLINE database through January 2017 for long-term studies of morbidity and air pollution and cataloged them with respect to cardiovascular, respiratory, cancer, diabetes, hospitalization, neurological, and pregnancy-birth endpoints. The catalog is presented as an online appendix. Associations with PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm), PM₁₀ (PM with an aerodynamic diameter <10 μm), and nitrogen dioxide (NO₂) were evaluated most frequently among the 417 ambient air quality studies identified. Associations with total suspended particles (TSP), carbon, ozone, sulfur, vehicular traffic, radon, and indoor air quality were also reported. I evaluated each study in terms of pollutant significance (yes, no), duration of exposure, and publication date. I found statistically significant pollutant relationships (P < 0.05) in 224 studies; 220 studies indicated adverse effects. Among 795 individual pollutant effect estimates, 396 are statistically significant. Pollutant associations with cardiovascular indicators, lung function, respiratory symptoms, and low birth weight are more likely to be significant than with disease incidence, heart attacks, diabetes, or neurological endpoints. Elemental carbon (EC), traffic, and PM_2.5 are most likely to be significant for cardiovascular outcomes; TSP, EC, and ozone (O₃) for respiratory outcomes; NO₂ for neurological outcomes; and PM₁₀ for birth/pregnancy outcomes. Durations of exposure range from 60 days to 35 yr, but I found no consistent relationships with the likelihood of statistical significance. Respiratory studies began ca. 1975; studies of diabetes, cardiovascular, and neurological effects increased after about 2005. I found 72 studies of occupational air pollution exposures; 40 reported statistically significant adverse health effects, especially for respiratory conditions. I conclude that the aggregate of these studies supports the existence of nonlethal physiological effects of various pollutants, more so for non–life-threatening endpoints and for noncriteria pollutants (TSP, EC, PM_2.5 metals). However, most studies were cross-sectional analyses over limited time spans with no consideration of lag or disease latency. Further longitudinal studies are thus needed to investigate the progress of disease incidence in association with air pollution exposure.

Implications: Relationships of air pollution with excess mortality are better known than with long-term antecedent morbidity. I cataloged 489 studies of cardiovascular, respiratory, cancer, and neurological effects, diabetes, and birth outcomes with respect to 12 air pollutants. About half of the studies reported statistically significant relationships, more frequently with noncriteria than with criteria pollutants. Indoor and cumulative exposures, coarse or ultrafine particles, and organic carbon were seldom considered. Significant relationships were more likely with less-severe endpoints such as blood pressure, lung function, or respiratory symptoms than with incidence of cancer, chronic obstructive pulmonary disease (COPD), heart failure, or diabetes. Most long-term studies are based on spatial relationships; longitudinal studies are needed to link the progression of pollution-related morbidity to mortality, especially for the cardiovascular system.     

Summer air quality over an artificial lake

In the summer of 1998, the air quality (indicators: CO, NO, NO₂, O₃) above the water surface of the Lake Balderey (Essen, Ruhr area, North Rhine-Westphalia, Germany), an artificial lake used for recreation purposes, was measured using the Fourier transform infrared spectroscopy (FTIR) and differential optical absorption spectroscopy (DOAS) remote measurement methods. The lake, with an area of 3 km² was created by damming the Ruhr and is surrounded by higher ground. In calm, bright weather conditions, this location results in a low-exchange situation (formation of temperature inversions, cold air dynamics) with a sustained impact on pollutant concentrations over the lake. The results of trace substance measurements (1/2 h mean values) were compared with values from comparison stations (suburban, high traffic and forest) located outside the area of the lake. In general, it was found that mean CO and NO concentrations over the lake were very low (0.3 ppm and 7.5 ppb, respectively). NO₂ values (15 ppb) were some 3.5 times higher than those recorded at the forest station and O₃ values, at 27 ppb, almost reached the same level as at the forest station (30 ppb). Mass flow densities as a function of wind direction, diurnal courses, differences between weekdays and weekends and comparisons with air quality standards are presented for the lake station.     

A multi-objective assessment of an air quality monitoring network using environmental,economic, and social indicators and GIS-based models

In the United States, air pollution is primarily measured by Air Quality Monitoring Networks (AQMN). These AQMNs have multiple objectives, including characterizing pollution patterns, protecting the public health, and determining compliance with air quality standards. In 2006, the U.S. Environmental Protection Agency issued a directive that air pollution agencies assess the performance of their AQMNs. Although various methods to design and assess AQMNs exist, here we demonstrate a geographic information system (GIS)-based approach that combines environmental, economic, and social indicators through the assessment of the ozone (O₃) and particulate matter (PM10) networks in Maricopa County, Arizona. The assessment was conducted in three phases: (1) to evaluate the performance of the existing networks, (2) to identify areas that would benefit from the addition of new monitoring stations, and (3) to recommend changes to the AQMN. A comprehensive set of indicators was created for evaluating differing aspects of the AQMNs’ objectives, and weights were applied to emphasize important indicators. Indicators were also classified according to their sustainable development goal. Our results showed that O₃ was well represented in the county with some redundancy in terms of the urban monitors. The addition of weights to the indicators only had a minimal effect on the results. For O₃, urban monitors had greater social scores, while rural monitors had greater environmental scores. The results did not suggest a need for adding more O₃ monitoring sites. For PM10, clustered urban monitors were redundant, and weights also had a minimal effect on the results. The clustered urban monitors had overall low scores; sites near point sources had high environmental scores. Several areas were identified as needing additional PM10 monitors. This study demonstrates the usefulness of a multi-indicator approach to assess AQMNs. Network managers and planners may use this method to assess the performance of air quality monitoring networks in urban regions.

Implications:The U.S. Environmental Protection Agency issued a directive in 2006 that air pollution agencies assess the performance of their AQMNs; as a result, we developed a GIS-based, multi-objective assessment approach that integrates environmental, economic, and social indicators, and demonstrates its use through assessing the O₃ and PM₁₀ monitoring networks in the Phoenix metropolitan area. We exhibit a method of assessing network performance and identifying areas that would benefit from new monitoring stations; also, we demonstrate the effect of adding weights to the indicators. Our study shows that using a multi-indicator approach gave detailed assessment results for the Phoenix AQMN.     

A holistic approach for optimal design of air quality monitoring network expansion in an urban area

This paper presents an objective methodology for determining the optimum number of ambient air quality stations in a monitoring network. The methodology integrates the multiple-criteria method with the spatial correlation technique. The pollutant concentration and population exposure data are used in this methodology in different ways. In the first stage, the Fuzzy Analytic Hierarchy Process (FAHP) with triangular fuzzy numbers (TFNs) is used to identify the most desirable monitoring locations. The network configuration is then determined on the basis of the concept of sphere of influences (SOIs). The SOIs are dictated by a predetermined cutoff value (r_c) in the spatial correlation coefficients (r) between the pollutant concentrations at the monitoring stations identified from first step and the corresponding concentrations at neighboring locations in the region. Finally, the optimal station locations are ranked by using combined utility scores gained from the first and second steps. The expansion of air quality monitoring network of Riyadh city in Saudi Arabia is used as a case study to demonstrate the proposed methodology.     

The relationship between atmospheric pollutant emissions and fuel qualities of inland vessels in Jiangsu Province,China

Air pollution caused by ship exhaust emission is receiving more and more attention. The physical and chemical properties of fuels, such as sulfur content and PAHs content, potentially had a significant influence on air pollutant emissions from inland vessels. In order to investigate the effects of fuel qualities on atmospheric pollutant emissions systematically, a series of experiments was conducted based on the method of actual ship testing. As a result, SO2, PM and NOx emission rates all increased with the increase of main engine rotating speed under cruise mode, while PM and NOx emission factors were inversely proportional to the main engine rotating speed. Moreover, SO2 emission factor changed little with the increase of the main engine rotating speed. In summary, the fuel-dependent specific emission of SO2 was a direct reflection of the sulfur content in fuel. The PM emission increased with the increase of sulfur content and PAHs content in fuel. However, fuel qualities impacted little on NOx emissions from inland vessels because of NOx formation mechanisms and conditions.

Implications: Ship activity is considered to be the third largest source of air pollution in China. In particular, air pollutants emitted from ships in river ports and waterways have a direct impact on regional air quality and pose threat on the health of local residents owing to high pollutants concentration and poor air diffusion. The study on the relationship between air pollutant emissions and fuel quality of inland vessels can provide foundational data for local authority to formulate reasonable and appropriate policies for reducing atmospheric pollution due to inland vessels.     

Analysis of short-term ozone and PM2.5 measurements: Characteristics and relationships for air sensor messaging

Air quality sensors are becoming increasingly available to the general public, providing individuals and communities with information on fine-scale, local air quality in increments as short as 1 min. Current health studies do not support linking 1-min exposures to adverse health effects; therefore, the potential health implications of such ambient exposures are unclear. The U.S. Environmental Protection Agency (EPA) establishes the National Ambient Air Quality Standards (NAAQS) and Air Quality Index (AQI) on the best science available, which typically uses longer averaging periods (e.g., 8 hr; 24 hr). Another consideration for interpreting sensor data is the variable relationship between pollutant concentrations measured by sensors, which are short-term (1 min to 1 hr), and the longer term averages used in the NAAQS and AQI. In addition, sensors often do not meet federal performance or quality assurance requirements, which introduces uncertainty in the accuracy and interpretation of these readings. This article describes a statistical analysis of data from regulatory monitors and new real-time technology from Village Green benches to inform the interpretation and communication of short-term air sensor data. We investigate the characteristics of this novel data set and the temporal relationships of short-term concentrations to 8-hr average (ozone) and 24-hr average (PM_2.5) concentrations to examine how sensor readings may relate to the NAAQS and AQI categories, and ultimately to inform breakpoints for sensor messages. We consider the empirical distributions of the maximum 8-hr averages (ozone) and 24-hr averages (PM_2.5) given the corresponding short-term concentrations, and provide a probabilistic assessment. The result is a robust, empirical comparison that includes events of interest for air quality exceedances and public health communication. Concentration breakpoints are developed for short-term sensor readings such that, to the extent possible, the related air quality messages that are conveyed to the public are consistent with messages related to the NAAQS and AQI.

Implications: Real-time sensors have the potential to provide important information about fine-scale current air quality and local air quality events. The statistical analysis of short-term regulatory and sensor data, coupled with policy considerations and known health effects experienced over longer averaging times, supports interpretation of such short-term data and efforts to communicate local air quality.     

Assessment of indoor levels of volatile organic compounds and carbon dioxide in schools in Kuwait

Indoor air quality (IAQ) in schools is a matter of concern because children are most vulnerable and sensitive to pollutant exposure. Conservation of energy at the expense of ventilation in heating, ventilation, and air conditioning (HVAC) systems adversely affects IAQ. Extensive use of new materials in building, fitting, and refurbishing emit various pollutants such that the indoor environment creates its own discomfort and health risks. Various schools in Kuwait were selected to assess their IAQ. Comprehensive measurements of volatile organic compounds (VOCs) consisting of 72 organic compounds consisting of aliphatic (C₃–C₆), aromatic (C₆–C₉), halogenated (C₁–C₇), and oxygenated (C₂–C₉) functional groups in indoor air were made for the first time in schools in Kuwait. The concentrations of indoor air pollutants revealed hot spots (science preparation rooms, science laboratories, arts and crafts classes/paint rooms, and woodworking shops/decoration rooms where local sources contributed to the buildup of pollutants in each school. The most abundant VOC pollutant was chlorodifluoromethane (R22; ClF₂CH), which leaked from air conditioning (AC) systems due to improper operation and maintenance. The other copious VOCs were alcohols and acetone at different locations due to improper handling of the chemicals and their excessive uses as solvents. Indoor carbon dioxide (CO₂) levels were measured, and these levels reflected the performance of HVAC systems; a specific rate or lack of ventilation affected the IAQ. Recommendations are proposed to mitigate the buildup of indoor air pollutants at school sites.

Implications: Indoor air quality in elementary schools has been a subject of extreme importance due to susceptibility and sensibility of children to air pollutants. The schools were selected based on their surrounding environment especially downwind direction from the highly industrialized zone in Kuwait. Extensive sampling from different sites in four schools for comprehensive VOCs and CO₂ were completed for an extended period of over a year. Different hot spots were identified where leaked refrigerant and inadequate handling of laboratory solvents contributed to the high VOCs in the respective locations. CO₂ levels reflected HVAC performance and poor ventilation. A list of recommendations has been proposed to eradicate these high levels of air pollution.     

Emissions from oil and gas operations in the United States and their air quality implications

The energy supply infrastructure in the United States has been changing dramatically over the past decade. Increased production of oil and natural gas, particularly from shale resources using horizontal drilling and hydraulic fracturing, made the United States the world’s largest producer of oil and natural gas in 2014. This review examines air quality impacts, specifically, changes in greenhouse gas, criteria air pollutant, and air toxics emissions from oil and gas production activities that are a result of these changes in energy supplies and use. National emission inventories indicate that volatile organic compound (VOC) and nitrogen oxide (NO_x) emissions from oil and gas supply chains in the United States have been increasing significantly, whereas emission inventories for greenhouse gases have seen slight declines over the past decade. These emission inventories are based on counts of equipment and operational activities (activity factors), multiplied by average emission factors, and therefore are subject to uncertainties in these factors. Although uncertainties associated with activity data and missing emission source types can be significant, multiple recent measurement studies indicate that the greatest uncertainties are associated with emission factors. In many source categories, small groups of devices or sites, referred to as super-emitters, contribute a large fraction of emissions. When super-emitters are accounted for, multiple measurement approaches, at multiple scales, produce similar results for estimated emissions. Challenges moving forward include identifying super-emitters and reducing their emission magnitudes. Work done to date suggests that both equipment malfunction and operational practices can be important. Finally, although most of this review focuses on emissions from energy supply infrastructures, the regional air quality implications of some coupled energy production and use scenarios are examined. These case studies suggest that both energy production and use should be considered in assessing air quality implications of changes in energy infrastructures, and that impacts are likely to vary among regions.

Implications: The energy supply infrastructure in the United States has been changing dramatically over the past decade, leading to changes in emissions from oil and natural gas supply chain sources. In many source categories along these supply chains, small groups of devices or sites, referred to as super-emitters, contribute a large fraction of emissions. Effective emission reductions will require technologies for both identifying super-emitters and reducing their emission magnitudes.     

Changes in weather and climate extremes: State of knowledge relevant to air and water quality in the United States

Air and water quality are impacted by extreme weather and climate events on time scales ranging from minutes to many months. This review paper discusses the state of knowledge of how and why extreme events are changing and are projected to change in the future. These events include heat waves, cold waves, floods, droughts, hurricanes, strong extratropical cyclones such as nor'easters, heavy rain, and major snowfalls. Some of these events, such as heat waves, are projected to increase, while others, with cold waves being a good example, will decrease in intensity in our warming world. Each extreme's impact on air or water quality can be complex and can even vary over the course of the event.

Implications:

Because extreme weather and climate events impact air and water quality, understanding how the various extremes are changing and are projected to change in the future has ramifications on air and water quality management.     

A comparison of hourly with annual air pollutant emissions: Implications for estimating acute exposure and public health risk

Health risks from air pollutants are evaluated by comparing chronic (i.e., an average over 1 yr or greater) or acute (typically 1-hr) exposure estimates with chemical- and duration-specific reference values or standards. When estimating long-term pollutant concentrations via exposure modeling, facility-level annual average emission rates are readily available as model inputs for most air pollutants. In contrast, there are far fewer facility-level hour-by-hour emission rates available for many of these same pollutants. In this report, we first analyze hour-by-hour emission rates for total reduced sulfur (TRS) compounds from eight kraft pulp mill operations. This data set is used to demonstrate discrepancies between estimating exposure based on a single TRS emission rate that has been calculated as the mean of all operating hours of the year, as opposed to reported hourly emission rates. A similar analysis is then performed using reported hourly emission rates for sulfur dioxide (SO₂) and oxides of nitrogen (NO_x) from three power generating units from a U.S. power plant. Results demonstrate greater variability at kraft pulp mill operations, with ratios of reported hourly to average hourly TRS emissions ranging from less than 1 to greater than 160 during routine facility operations. Thus, if fluctuations in hourly emission rates are not accounted for, over- or underestimates of hourly exposure, and thus acute health risk, may occur. In addition to this analysis, we also demonstrate an additional challenge when assessing health risk based on hourly exposures: the lack of human health reference values based on 1-hr exposures.

Implications: Largely due to the lack of reported hourly emission rate data for many air pollutants, an hourly average emission rate (calculated from an annual emission rate) is often used when modeling the potential for acute health risk. We calculated ratios between reported hourly and hourly average emission rates from pulp and paper mills and a U.S. power plant to demonstrate that if not considered, hourly fluctuations in emissions could result in an over- or underestimation of exposure and risk. We also demonstrate the lack of 1-hr human health reference values meant to be protective of the general population, including children.     

Autonomous mobile platform for monitoring air emissions from industrial and municipal wastewater ponds

Significant amounts of volatile organic compounds and greenhouse gases are generated from wastewater lagoons and tailings ponds in Alberta, Canada. Accurate measurements of these air pollutants and greenhouse gases are needed to support management and regulatory decisions. A mobile platform was developed to measure air emissions from tailings pond in the oil sands region of Alberta. The mobile platform was tested in 2015 in a municipal wastewater treatment lagoon. With a flux chamber and a CO₂/CH₄ sensor on board, the mobile platform was able to measure CO₂ and CH₄ emissions over two days at two different locations in the pond. Flux emission rates of CO₂ and CH₄ that were measured over the study period suggest the presence of aerobic and anaerobic zones in the wastewater treatment lagoon. The study demonstrated the capabilities of the mobile platform in measuring fugitive air emissions and identified the potential for the applications in air and water quality monitoring programs.

Implications: The Mobile Platform demonstrated in this study has the ability to measure greenhouse gas (GHG) emissions from fugitive sources such as municipal wastewater lagoons. This technology can be used to measure emission fluxes from tailings ponds with better detection of spatial and temporal variations of fugitive emissions. Additional air and water sampling equipment could be added to the mobile platform for a broad range of air and water quality studies in the oil sands region of Alberta.     

Detection of anomalous nitrogen dioxide (NO2) concentration in urban air of India using proximity and clustering methods

Owing to accurate future air quality estimates, need for detecting the anomalously high increase in concentration of pollutants cannot be adjourned. Plentiful approaches were proposed in the past to substantially determine the abnormal conditions, but most of the statistical approaches were computationally expensive and ignored the false alarm ratios. Thus, a hybrid of proximity- and clustering-based anomaly detection approaches to identify anomalies in the air quality data is suggested in this work. The Gaussian distribution property of the real-world data set is utilized further to segregate out anomalies. The results depicted twofold advantages of our approach, by efficient extraction of anomalies and with increased accuracy by reducing the number of false alarms. Specifically, the presence of NO₂ concentration in air is investigated in this work, considering its constant increase over decades as well as its inevitable health risks. Furthermore, spatiotemporal segments with anomalously high NO₂ concentrations for 14 residential, industrial, and commercial areas of five cities in India are extracted. To validate the results, a comparative analysis with existing approaches of anomaly detection and with two benchmark data sets is performed. Results showed that our method outperformed the existing methods of anomaly detection, when evaluated over metrics such as sensitivity, miss rate, and false alarms. Further, a detailed analysis of extracted anomalies and a detailed discussion about the factors responsible for such anomalies are presented in this work. This study is helpful in educating government and people about spatiotemporal, geographical, and economic conditions responsible for anomalously high NO₂ concentrations in air.

Implications: Using our methodology, days with extremely high concentration of any pollutant in air, at any particular location, can be extracted. The reasons for such extremely high pollutant concentration on particular days of a year can be studied and preventive measures can be taken by the government. Thus, by identification of causes of anomalies, future similar events can be avoided. This would also help in people’s decision making in case such events occur in the future.