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.     

Air pollutant emissions from the development,production, and processing of Marcellus Shale natural gas

The Marcellus Shale is one of the largest natural gas reserves in the United States; it has recently been the focus of intense drilling and leasing activity. This paper describes an air emissions inventory for the development, production, and processing of natural gas in the Marcellus Shale region for 2009 and 2020. It includes estimates of the emissions of oxides of nitrogen (NO_x), volatile organic compounds (VOCs), and primary fine particulate matter (≤2.5 µm aerodynamic diameter; PM_2.5) from major activities such as drilling, hydraulic fracturing, compressor stations, and completion venting. The inventory is constructed using a process-level approach; a Monte Carlo analysis is used to explicitly account for the uncertainty. Emissions were estimated for 2009 and projected to 2020, accounting for the effects of existing and potential additional regulations. In 2020, Marcellus activities are predicted to contribute 6–18% (95% confidence interval) of the NO_x emissions in the Marcellus region, with an average contribution of 12% (129 tons/day). In 2020, the predicted contribution of Marcellus activities to the regional anthropogenic VOC emissions ranged between 7% and 28% (95% confidence interval), with an average contribution of 12% (100 tons/day). These estimates account for the implementation of recently promulgated regulations such as the Tier 4 off-road diesel engine regulation and the U.S. Environmental Protection Agency's (EPA) Oil and Gas Rule. These regulations significantly reduce the Marcellus VOC and NO_x emissions, but there are significant opportunities for further reduction in these emissions using existing technologies.

Implications: The Marcellus Shale is one of the largest natural gas reserves in United States. The development and production of this gas may emit substantial amounts of oxides of nitrogen and volatile organic compounds. These emissions may have special significance because Marcellus development is occurring close to areas that have been designated nonattainment for the ozone standard. Control technologies exist to substantially reduce these impacts. PM_2.5 emissions are predicted to be negligible in a regional context, but elemental carbon emissions from diesel powered equipment may be important.     

Evaluation of gas well setback policy in the Marcellus Shale region of Pennsylvania in relation to emissions of fine particulate matter

Shale gas has become an important strategic energy source with considerable potential economic benefits and the potential to reduce greenhouse gas emissions in so far as it displaces coal use. However, there still exist environmental health risks caused by emissions from exploration and production activities. In the United States, states and localities have set different minimum setback policies to reduce the health risks corresponding to the emissions from these locations, but it is unclear whether these policies are sufficient. This study uses a Gaussian plume model to evaluate the probability of exposure exceedance from EPA concentration limits for PM2.5 at various locations around a generic wellsite in the Marcellus shale region. A set of meteorological data monitored at ten different stations across Marcellus shale gas region in Pennsylvania during 2015 serves as an input to this model. Results indicate that even though the current setback distance policy in Pennsylvania (500 ft. or 152.4 m) might be effective in some cases, exposure limit exceedance occurs frequently at this distance with higher than average emission rates and/or greater number of wells per wellpad. Setback distances should be 736 m to ensure compliance with the daily average concentration of PM2.5, and a function of the number of wells to comply with the annual average PM2.5 exposure standard.

Implications: The Marcellus Shale gas is known as a significant source of criteria pollutants and studies show that the current setback distance in Pennsylvania is not adequate to protect the residents from exceeding the established limits. Even an effective setback distance to meet the annual exposure limit may not be adequate to meet the daily limit. The probability of exceeding the annual limit increases with number of wells per site. We use a probabilistic dispersion model to introduce a technical basis to select appropriate setback distances.     

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.     

An exploratory study of air emissions associated with shale gas development and production in the Barnett Shale

Information regarding air emissions from shale gas extraction and production is critically important given production is occurring in highly urbanized areas across the United States. Objectives of this exploratory study were to collect ambient air samples in residential areas within 61 m (200 feet) of shale gas extraction/production and determine whether a “fingerprint” of chemicals can be associated with shale gas activity. Statistical analyses correlating fingerprint chemicals with methane, equipment, and processes of extraction/production were performed. Ambient air sampling in residential areas of shale gas extraction and production was conducted at six counties in the Dallas/Fort Worth (DFW) Metroplex from 2008 to 2010. The 39 locations tested were identified by clients that requested monitoring. Seven sites were sampled on 2 days (typically months later in another season), and two sites were sampled on 3 days, resulting in 50 sets of monitoring data. Twenty-four-hour passive samples were collected using summa canisters. Gas chromatography/mass spectrometer analysis was used to identify organic compounds present. Methane was present in concentrations above laboratory detection limits in 49 out of 50 sampling data sets. Most of the areas investigated had atmospheric methane concentrations considerably higher than reported urban background concentrations (1.8–2.0 ppm_v). Other chemical constituents were found to be correlated with presence of methane. A principal components analysis (PCA) identified multivariate patterns of concentrations that potentially constitute signatures of emissions from different phases of operation at natural gas sites. The first factor identified through the PCA proved most informative. Extreme negative values were strongly and statistically associated with the presence of compressors at sample sites. The seven chemicals strongly associated with this factor (o-xylene, ethylbenzene, 1,2,4-trimethylbenzene, m- and p-xylene, 1,3,5-trimethylbenzene, toluene, and benzene) thus constitute a potential fingerprint of emissions associated with compression.

Implications: Information regarding air emissions from shale gas development and production is critically important given production is now occurring in highly urbanized areas across the United States. Methane, the primary shale gas constituent, contributes substantially to climate change; other natural gas constituents are known to have adverse health effects. This study goes beyond previous Barnett Shale field studies by encompassing a wider variety of production equipment (wells, tanks, compressors, and separators) and a wider geographical region. The principal components analysis, unique to this study, provides valuable information regarding the ability to anticipate associated shale gas chemical constituents.     

Air quality mapping using GIS and economic evaluation of health impact for Mumbai City,India

Mumbai, a highly populated city in India, has been selected for air quality mapping and assessment of health impact using monitored air quality data. Air quality monitoring networks in Mumbai are operated by National Environment Engineering Research Institute (NEERI), Maharashtra Pollution Control Board (MPCB), and Brihanmumbai Municipal Corporation (BMC). A monitoring station represents air quality at a particular location, while we need spatial variation for air quality management. Here, air quality monitored data of NEERI and BMC were spatially interpolated using various inbuilt interpolation techniques of ArcGIS. Inverse distance weighting (IDW), Kriging (spherical and Gaussian), and spline techniques have been applied for spatial interpolation for this study. The interpolated results of air pollutants sulfur dioxide (SO₂), nitrogen dioxide (NO₂) and suspended particulate matter (SPM) were compared with air quality data of MPCB in the same region. Comparison of results showed good agreement for predicted values using IDW and Kriging with observed data. Subsequently, health impact assessment of a ward was carried out based on total population of the ward and air quality monitored data within the ward. Finally, health cost within a ward was estimated on the basis of exposed population. This study helps to estimate the valuation of health damage due to air pollution.

Implications: Operating more air quality monitoring stations for measurement of air quality is highly resource intensive in terms of time and cost. The appropriate spatial interpolation techniques can be used to estimate concentration where air quality monitoring stations are not available. Further, health impact assessment for the population of the city and estimation of economic cost of health damage due to ambient air quality can help to make rational control strategies for environmental management. The total health cost for Mumbai city for the year 2012, with a population of 12.4 million, was estimated as USD8000 million.     

Air quality modeling for effective environmental management in the mining region

Air quality in the mining sector is a serious environmental concern and associated with many health issues. Air quality management in mining regions has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanisms. A modeling approach called the mining air dispersion model (MADM) is developed to predict air pollutants concentration in the mining region while considering the deposition effect. The model takes into account the planet’s boundary conditions and assumes that the eddy diffusivity depends on the downwind distance. The developed MADM is applied to a mining site in Canada. The model provides values for the predicted concentrations of PM₁₀, PM_2.5, TSP, NO₂, and six heavy metals (As, Pb, Hg, Cd, Zn, Cr) at various receptor locations. The model shows that neutral stability conditions are dominant for the study site. The maximum mixing height is achieved (1280 m) during the evening in summer, and the minimum mixing height (380 m) is attained during the evening in winter. The dust fall (PM coarse) deposition flux is maximum during February and March with a deposition velocity of 4.67 cm/sec. The results are evaluated with the monitoring field values, revealing a good agreement for the target air pollutants with R-squared ranging from 0.72 to 0.96 for PM_2.5, from 0.71 to 0.82 for PM₁₀, and from 0.71 to 0.89 for NO₂. The analyses illustrate that the presented algorithm in this model can be used to assess air quality for the mining site in a systematic way. Comparisons of MADM and CALPUFF modeling values are made for four different pollutants (PM_2.5, PM₁₀, TSP, and NO₂) under three different atmospheric stability classes (stable, neutral, and unstable). Further, MADM results are statistically tested against CALPUFF for the air pollutants and model performance is found satisfactory.

Implications: The mathematical model (MADM) is developed by extending the Gaussian equation particularly when examining the settling process of important pollutants for the industrial region. Physical removal effects of air pollutants with field data have been considerred for the MADM development and for an extensive field case study. The model is well validated in the field of an open pit mine to assess the regional air quality. The MADA model helps to facilitate the management of the mining industry in doing estimation of emission rate around mining activities and predicting the resulted concentration of air pollutants together in one integrated approach.     

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.     

Ambient air concentrations exceeded health-based standards for fine particulate matter and benzene during the Deepwater Horizon oil spill

The Deepwater Horizon oil spill is considered one of the largest marine oil spills in the history of the United States. Air emissions associated with the oil spill caused concern among residents of Southeast Louisiana. The purpose of this study was to assess ambient concentrations of benzene (n=3,887) and fine particulate matter (n=102,682) during the oil spill and to evaluate potential exposure disparities in the region. Benzene and fine particulate matter (PM2.5) concentrations in the targeted parishes were generally higher following the oil spill, as expected. Benzene concentrations reached 2 to 19 times higher than background, and daily exceedances of PM2.5 were 10 to 45 times higher than background. Both benzene and PM2.5 concentrations were considered high enough to exceed public health criteria, with measurable exposure disparities in the coastal areas closer to the spill and clean-up activities. These findings raise questions about public disclosure of environmental health risks associated with the oil spill. The findings also provide a science-based rationale for establishing health-based action levels in future disasters.

Implications: Benzene and particulate matter monitoring during the Deepwater Horizon oil spill revealed that ambient air quality was a likely threat to public health and that residents in coastal Louisiana experienced significantly greater exposures than urban residents. Threshold air pollution levels established for the oil spill apparently were not used as a basis for informing the public about these potential health impacts. Also, despite carrying out the most comprehensive air monitoring ever conducted in the region, none of the agencies involved provided integrated analysis of the data or conclusive statements about public health risk. Better information about real-time risk is needed in future environmental disasters.     

Measurement of atmospheric pollutants associated with oil and natural gas exploration and production activity in Pennsylvania’s Allegheny National Forest

Oil and natural gas exploration and production (E&P) activities generate emissions from diesel engines, compressor stations, condensate tanks, leaks and venting of natural gas, construction of well pads, and well access roads that can negatively impact air quality on both local and regional scales. A mobile, autonomous air quality monitoring laboratory was constructed to collect measurements of ambient concentrations of pollutants associated with oil and natural gas E&P activities. This air-monitoring laboratory was deployed to the Allegheny National Forest (ANF) in northwestern Pennsylvania for a campaign that resulted in the collection of approximately 7 months of data split between three monitoring locations between July 2010 and June 2011. The three monitoring locations were the Kane Experimental Forest (KEF) area in Elk County, which is downwind of the Sackett oilfield; the Bradford Ranger Station (BRS) in McKean County, which is downwind of a large area of historic oil and gas productivity; and the U.S. Forest Service Hearts Content campground (HC) in Warren County, which is in an area relatively unimpacted by oil and gas development and which therefore yielded background pollutant concentrations in the ANF. Concentrations of criteria pollutants ozone and NO₂ did not vary significantly from site to site; averages were below National Ambient Air Quality Standards. Concentrations of volatile organic compounds (VOCs) associated with oil and natural gas (ethane, propane, butane, pentane) were highly correlated. Applying the conditional probability function (CPF) to the ethane data yielded most probable directions of the sources that were coincident with known location of existing wells and activity. Differences between the two impacted and one background site were difficult to discern, suggesting the that the monitoring laboratory was a great enough distance downwind of active areas to allow for sufficient dispersion with background air such that the localized plumes were not detected.

ImplicationsMonitoring of pollutants associated with oil and natural gas exploration and production activity at three sites within the Allegheny National Forest (ANF) showed only slight site-to-site differences even with one site far removed from these activities. However, the impact was evident not in detection of localized plumes but in regional elevated ethane concentrations, as ethane can be considered a tracer species for oil and natural gas activity. The data presented serve as baseline conditions for evaluation of impacts from future development of Marcellus or Utica shale gas reserves.     

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.     

Urban heat island (UHI) influence on secondary pollutant formation in a tropical humid environment

The combined action of urbanization (change in land use) and increase in vehicular emissions intensifies the urban heat island (UHI) effect in many cities in the developed countries. The urban warming (UHI) enhances heat-stress-related diseases and ozone (O₃) levels due to a photochemical reaction. Even though UHI intensity depends on wind speed, wind direction, and solar flux, the thermodynamic properties of surface materials can accelerate the temperature profiles at the local scale. This mechanism modifies the atmospheric boundary layer (ABL) structure and mixing height in urban regions. These changes further deteriorate the local air quality. In this work, an attempt has been made to understand the interrelationship between air pollution and UHI intensity at selected urban areas located at tropical environment. The characteristics of ambient temperature profiles associated with land use changes in the different microenvironments of Chennai city were simulated using the Envi-Met model. The simulated surface 24-hr average air temperatures (11 m above the ground) for urban background and commercial and residential sites were found to be 30.81 ± 2.06, 31.51 ± 1.87, and 31.33 ± 2.1ºC, respectively. The diurnal variation of UHI intensity was determined by comparing the daytime average air temperatures to the diurnal air temperature for different wind velocity conditions. From the model simulations, we found that wind speed of 0.2 to 5 m/sec aggravates the UHI intensity. Further, the diurnal variation of mixing height was also estimated at the study locations. The estimated lowest mixing height at the residential area was found to be 60 m in the middle of night. During the same period, highest ozone (O₃) concentrations were also recorded at the continuous ambient air quality monitoring station (CAAQMS) located at the residential area.

Implications: An attempt has made to study the diurnal variation of secondary pollution levels in different study regions. This paper focuses mainly on the UHI intensity variations with respect to percentage of land use pattern change in Chennai city, India. The study simulated the area-based land use pattern with local mixing height variations. The relationship between UHI intensity and mixing height provides variations on local air quality.     

Evaluation of air quality zone classification methods based on ambient air concentration exposure

Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure. The purpose of this paper is to compare the risk associated with selecting one ambient air classification approach over another by testing the possible exposure an individual living within a zone may face. The chronic daily intake (CDI) is utilized to compare different pollutant exposures over the classification duration of 3 years between two classification methods. Historical data collected from air monitoring stations in Kuwait are used to build representative models of 1-hr NO₂ and 8-hr O₃ within a zone that meets the compliance requirements of each method. The first method, the “3 Strike” method, is a conservative approach based on a winner-take-all approach common with most compliance classification methods, while the second, the 99% Rule method, allows for more robust analyses and incorporates long-term trends. A Monte Carlo analysis is used to model the CDI for each pollutant and each method with the zone at a single station and with multiple stations. The model assumes that the zone is already in compliance with air quality standards over the 3 years under the different classification methodologies. The model shows that while the CDI of the two methods differs by 2.7% over the exposure period for the single station case, the large number of samples taken over the duration period impacts the sensitivity of the statistical tests, causing the null hypothesis to fail. Local air quality managers can use either methodology to classify the compliance of an air zone, but must accept that the 99% Rule method may cause exposures that are statistically more significant than the 3 Strike method.

Implications: A novel method using the Central Limit Theorem and Monte Carlo analysis is used to directly compare different air standard compliance classification methods by estimating the chronic daily intake of pollutants. This method allows air quality managers to rapidly see how individual classification methods may impact individual population groups, as well as to evaluate different pollutants based on dosage and exposure when complete health impacts are not known.     

Particulate matter,nitrogen oxides,ozone, and select volatile organic compounds during a winter sampling period in Logan,Utah, USA

Particulate matter mass (PM), trace gaseous pollutants, and select volatile organic compounds (VOCs) with meteorological variables were measured in Logan, Utah (Cache Valley), for >4 weeks during winter 2017 as part of the Utah Winter Fine Particle Study (UWFPS). Higher PM levels for short time periods and lower ozone (O₃) levels were present due to meteorological and mountain valley conditions. Nitrogenous pollutants were relatively strongly correlated with PM variables. Diurnal cycles of NO_x, O₃, and fine PM(PM 2.5) (aerodynamic diameter <2.5 μm [PM_2.5]) suggested formation from NO_x. O₃ levels increased from early morning into midafternoon, and NO_x and PM_2.5 increased throughout the morning, followed by sharp decreases. Toluene/benzene and xylenes/benzene ratios and VOC correlations with nitrogenous and PM species were indicative of local traffic sources. Wind sector comparisons suggested that pollutant levels were lower when winds were from nearby mountains to the east versus winds from northerly or southerly origins.

Implications: The Cache Valley in Idaho and Utah has been designated a PM_2.5 nonattainment area that has been attributed to air pollution buildup during winter stagnation events. To inform state implementation plans for PM_2.5 in Cache Valley and other PM_2.5 nonattainment areas in Utah, a state and multiagency federal research effort known as the UWFPS was conducted in winter 2017. As part of the UWFPS, the U.S. Environmental Protection Agency (EPA) measured ground-based PM species and their precursors, VOCs, and meteorology in Logan, Utah. Results reported here from the EPA study in Logan provide additional understanding of wintertime air pollution conditions and possible sources of PM and gaseous pollutants as well as being useful for future PM control strategies in this area.     

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial,California, USA

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM_2.5 and PM₁₀ (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R² for converted hourly averaged Dylos mass measurements versus a PM_2.5 BAM was 0.79 and that versus a PM₁₀ BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM_2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R² = 0.35–0.81).

Implications: The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.     

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.     

Microscale air quality impacts of distributed power generation facilities

The electric system is experiencing rapid growth in the adoption of a mix of distributed renewable and fossil fuel sources, along with increasing amounts of off-grid generation. New operational regimes may have unforeseen consequences for air quality. A three-dimensional microscale chemical transport model (CTM) driven by an urban wind model was used to assess gaseous air pollutant and particulate matter (PM) impacts within ~10 km of fossil-fueled distributed power generation (DG) facilities during the early afternoon of a typical summer day in Houston, TX. Three types of DG scenarios were considered in the presence of motor vehicle emissions and a realistic urban canopy: (1) a 25-MW natural gas turbine operating at steady state in either simple cycle or combined heating and power (CHP) mode; (2) a 25-MW simple cycle gas turbine undergoing a cold startup with either moderate or enhanced formaldehyde emissions; and (3) a data center generating 10 MW of emergency power with either diesel or natural gas-fired backup generators (BUGs) without pollution controls. Simulations of criteria pollutants (NO₂, CO, O₃, PM) and the toxic pollutant, formaldehyde (HCHO), were conducted assuming a 2-hr operational time period. In all cases, NO_x titration dominated ozone production near the source. The turbine scenarios did not result in ambient concentration enhancements significantly exceeding 1 ppbv for gaseous pollutants or over 1 µg/m³ for PM after 2 hr of emission, assuming realistic plume rise. In the case of the datacenter with diesel BUGs, ambient NO₂ concentrations were enhanced by 10–50 ppbv within 2 km downwind of the source, while maximum PM impacts in the immediate vicinity of the datacenter were less than 5 µg/m³.

Implications: Plausible scenarios of distributed fossil generation consistent with the electricity grid’s transformation to a more flexible and modernized system suggest that a substantial amount of deployment would be required to significantly affect air quality on a localized scale. In particular, natural gas turbines typically used in distributed generation may have minor effects. Large banks of diesel backup generators such as those used by data centers, on the other hand, may require pollution controls or conversion to natural gas-fired reciprocal internal combustion engines to decrease nitrogen dioxide pollution.     

Impact of emissions from natural gas production facilities on ambient air quality in the Barnett Shale area: A pilot study

Rapid and extensive development of shale gas resources in the Barnett Shale region of Texas in recent years has created concerns about potential environmental impacts on water and air quality. The purpose of this study was to provide a better understanding of the potential contributions of emissions from gas production operations to population exposure to air toxics in the Barnett Shale region. This goal was approached using a combination of chemical characterization of the volatile organic compound (VOC) emissions from active wells, saturation monitoring for gaseous and particulate pollutants in a residential community located near active gas/oil extraction and processing facilities, source apportionment of VOCs measured in the community using the Chemical Mass Balance (CMB) receptor model, and direct measurements of the pollutant gradient downwind of a gas well with high VOC emissions. Overall, the study results indicate that air quality impacts due to individual gas wells and compressor stations are not likely to be discernible beyond a distance of approximately 100 m in the downwind direction. However, source apportionment results indicate a significant contribution to regional VOCs from gas production sources, particularly for lower-molecular-weight alkanes (<C6). Although measured ambient VOC concentrations were well below health-based safe exposure levels, the existence of urban-level mean concentrations of benzene and other mobile source air toxics combined with soot to total carbon ratios that were high for an area with little residential or commercial development may be indicative of the impact of increased heavy-duty vehicle traffic related to gas production

ImplicationsRapid and extensive development of shale gas resources in recent years has created concerns about potential environmental impacts on water and air quality. This study focused on directly measuring the ambient air pollutant levels occurring at residential properties located near natural gas extraction and processing facilities, and estimating the relative contributions from gas production and motor vehicle emissions to ambient VOC concentrations. Although only a small-scale case study, the results may be useful for guidance in planning future ambient air quality studies and human exposure estimates in areas of intensive shale gas production.     

Assessment of personal integrated exposure to fine particulate matter of urban residents in Hong Kong

Metropolitan residents are concerned about their exposure to airborne pollutants. But establishing these exposures is challenging. A compact personal exposure kit (PEK) was developed to evaluate personal integrated exposure (PIE) from time-resolved data to particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) in five microenvironments, including office, home, commuting, other indoor activities (other than home and office), and outdoor activities experienced both on weekdays and weekends. The study was conducted in Hong Kong. The PEK measured PM_2.5, reported location and several other factors, stored collected data, as well as reported the data back to the investigators using global system for mobile communication (GSM) telemetry. Generally, PM_2.5 concentrations in office microenvironment were found to be the smallest (13.0 μg/m³), whereas the largest PM_2.5 concentration microenvironments were experienced during outdoor activities (54.4 μg/m³). Participants spent more than 85% of their time indoors, including in offices, homes, and other public indoor venues. On average, 42% and 81% of the time were spent in homes, which contributed 52% and 79% of PIE (during weekdays and weekends, respectively), suggesting that improvement of air quality in homes may reduce overall exposures and indicating the need for actions to mitigate possible public health burdens in Hong Kong. This study also found that various indoor/outdoor microenvironments experienced by urban office workers cannot be accurately represented by general urban air quality data reported from the regulatory monitoring. Such personalized air quality information, especially while in transit or in offices and homes, may provide improved information on population exposures to air pollution.

Implications: A newly developed personal exposure kit (PEK) was used to monitor PM_2.5 exposure of metropolitan citizens in their daily life. Different microenvironments and time durations caused various personal integrated exposure (PIE). The stationary monitoring method for PIE was also compared and evaluated with PEK. Positive protection actions can be taken after understanding the major contribution to PM_2.5 exposure.     

Emission factors of fine particulate matter,organic and elemental carbon,carbon monoxide,and carbon dioxide for four solid fuels commonly used in residential heating by the U.S. Navajo Nation

Most homes in the Navajo Nation use wood as their primary heating fuel, often in combination with locally mined coal. Previous studies observed health effects linked to this solid-fuel use in several Navajo communities. Emission factors (EFs) for common fuels used by the Navajo have not been reported using a relevant stove type. In this study, two softwoods (ponderosa pine and Utah juniper) and two high-volatile bituminous coals (Black Mesa and Fruitland) were tested with an in-use residential conventional wood stove (homestove) using a modified American Society for Testing and Materials/U.S. Environmental Protection Agency (ASTM/EPA) protocol. Filter sampling quantified PM_2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) and organic (OC) and elemental (EC) carbon in the emissions. Real-time monitoring quantified carbon monoxide (CO), carbon dioxide (CO₂), and total suspended particles (TSP). EFs for these air pollutants were developed and normalized to both fuel mass and energy consumed. In general, coal had significantly higher mass EFs than wood for all pollutants studied. In particular, coal emitted, on average, 10 times more PM_2.5 than wood on a mass basis, and 2.4 times more on an energy basis. The EFs developed here were based on fuel types, stove design, and operating protocols relevant to the Navajo Nation, but they could be useful to other Native Nations with similar practices, such as the nearby Hopi Nation.

Implications: Indoor wood and coal combustion is an important contributor to public health burdens in the Navajo Nation. Currently, there exist no emission factors representative of Navajo homestoves, fuels, and practices. This study developed emission factors for PM_2.5, OC, EC, CO, and CO₂ using a representative Navajo homestove. These emission factors may be utilized in regional-, national-, and global-scale health and environmental models. Additionally, the protocols developed and results presented here may inform on-going stove design of the first EPA-certified wood and coal combination stove.