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Delhi is one among the highly air polluted cities in the world. Absence of causal relationship between emitting sources of PM 2.5 and their impact has resulted in inadequate actions. This research combines a set of innovative and state-of-the-art analytical techniques to establish relative predominance of PM 2.5 sources. Air quality sampling at six sites in summer and winter for 40 days (at each site) showed alarmingly high PM 2.5 concentrations (340 ± 135 μg/m 3 ). The collected PM 2.5 was subjected to chemical speciation including ions, metals, organic and elemental carbons which followed application of chemical mass balance technique for source apportionment. The source apportionment results showed that secondary aerosols, biomass burning (BMB), vehicles, fugitive dust, coal and fly ash, and municipal solid waste burning were the important sources. It was observed that secondary aerosol and crustal matter accounted for over 50% of mass. The PM 2.5 levels were not solely result of emissions from Delhi; it is a larger regional problem caused by contiguous urban agglomerations. It was argued that emission reduction of precursors of secondary aerosol, SO 2 , NO x , and volatile organic compounds, which are unabated, is essential. A substantial reduction in BMB and suspension of crustal dust is equally important to ensure compliance with air quality standards.

This study integrates the relationship between measured surface concentrations of particulate matter 10 μm or less in diameter (PM 10 ), satellite-derived aerosol optical depth (AOD), and meteorology in Roda, Virginia, during 2008. A multiple regression model was developed to predict the concentrations of particles 2.5 μm or less in diameter (PM 2.5 ) at an additional location in the Appalachia region, Bristol, TN. The model was developed by combining AOD retrievals from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on board the EOS Terra and Aqua Satellites with the surface meteorological observations. The multiple regression model predicted PM 2.5 (r 2 = 0.62), and the two-variable (AOD-PM 2.5 ) model predicted PM 2.5 (r 2 = 0.4). The developed model was validated using particulate matter recordings and meteorology observations from another location in the Appalachia region, Hazard, Kentucky. The model was extrapolated to the Roda, VA, sampling site to predict PM 2.5 mass concentrations. We used 10 km x 10 km resolution MODIS 550 nm AOD to predict ground level PM 2.5 . For the relevant period in 2008, in Roda, VA, the predicted PM 2.5 mass concentration is 9.11 ± 5.16 μg m -3 (mean ± 1SD)., Implications: This is the first study that couples ground-based Particulate Matter measurements with satellite retrievals to predict surface air pollution at Roda, Virginia. Roda is representative of the Appalachian communities that are commonly located in narrow valleys, or "hollows," where homes are placed directly along the roads in a region of active mountaintop mining operations. Our study suggests that proximity to heavy coal truck traffic subjects these communities to chronic exposure to coal dust and leads us to conclude that there is an urgent need for new regulations to address the primary sources of this particulate matter.

An analysis of fine particulate data in eastern North Carolina was conducted to investigate the impact of the hog industry and its emissions of ammonia into the atmosphere. The fine particulate data are simulated using ISORROPIA, an equilibrium thermodynamic model that simulates the gas and aerosol equilibrium of inorganic atmospheric species. The observational data analyses show that the major constituents of fine particulate matter (PM2.5) are organic carbon, elemental carbon, sulfate, nitrate, and ammonium. The observed PM2.5 concentration is positively correlated with temperature but anticorrelated with wind speed. The correlation between PM2.5 and wind direction at some locations suggests an impact of ammonia emissions from hog facilities on PM2.5 formation. The modeled results are in good agreement with observations, with slightly better agreement at urban sites than at rural sites. The predicted total inorganic particulate matter (PM) concentrations are within 5% of the observed values under conditions with median initial total PM species concentrations, median relative humidity (RH), and median temperature. Ambient conditions with high PM precursor concentrations, low temperature, and high RH appear to favor the formation of secondary PM.

The southeastern portion of North Carolina features a dense crop and animal agricultural region; previous research suggests that this agricultural presence emits a significant portion of the state's nitrogen (i.e., oxides of nitrogen and ammonia) emissions. These findings indicate that transporting air over this region can affect nitrogen concentrations in precipitation at sites as far as 50 mi away. The study combined nitrate nitrogen isotope data with back-trajectory analysis to examine the relationship between regional nitrogen emission estimates independent of pollutant concentration information. In 2004, the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to determine potential sources of nitrogen in rainwater collected at an urban receptor site in Raleigh, NC. The delta 15N isotope ratio signatures of each sample were used to further differentiate between sources of the rainwater nitrate. This study examined the importance of pollution sources, including animal agricultural activity, and meteorology on rainfall chemistry as well as the implications in fine particulate matter (PM2.5) formation. Samples that transited the dense crop and animal (swine) agricultural region of east-southeastern North Carolina (i.e., the source region) had lower delta 15N isotope ratios in the nitrate ion (average = -2.1 +/- 1.7 per thousand) than those from a counterpart nonagricultural region (average = 0.1 +/- 3 per thousand.) An increase in PM2.5 concentrations in the urban receptor site (yearly average = 15.1 +/- 5.8 microg/m3) was also found to correspond to air transport over the dense agricultural region relative to air that was not subjected to such transport (yearly average = 11.7 +/- 5.8 microg/m3).

Intensive animal agriculture is an important part of the US and North Carolina's (NC's) economy. Large emissions of ammonia (NH3) gas emanate from the handling of animal wastes at these operations contributing to the formation of fine particulate matter (PM2.5) around the state causing a variety of human health and environmental effects. The objective of this research is to provide the relationship between ammonia, aerosol optical depth and meteorology and its effect on PM2.5 concentrations using satellite observations (column ammonia and aerosol optical depth (AOD)) and ground-based meteorological observations. An observational-based multiple linear regression model was derived to predict ground-level PM2.5 during the summer months (JJA) from 2008–2017 in New Hanover County, Catawba County and Sampson County. A combination of the Cumberland and Johnston County models for the summer was chosen and validated for Duplin County, NC, then used to predict Sampson County, NC, PM2.5 concentrations. The model predicted a total of six 24 h exceedances over the nine-year period. This indicates that there are rural areas of the state that may have air quality issues that are not captured for a lack of measurements. Moreover, PM2.5 chemical composition analysis suggests that ammonium is a major component of the PM2.5 aerosol. [ABSTRACT FROM AUTHOR]

Ammonia (NH3) in the atmosphere contributes to the formation of airborne fine particulate matter (PM2.5), which is associated with adverse human health effects. The emission, transport, reactions, and deposition of NH3 in the atmosphere are modeled using the Community Multiscale Air Quality (CMAQ) model, within the U.S. National Air Quality Forecast Capability (NAQFC). The purpose of this current work is to evaluate the capability of the NAQFC CMAQ model and to identify potential improvements to NH3 emissions estimates and prediction methods. This study focuses on CMAQ predictions of atmospheric NH3 in North Carolina, including a region with intensive animal production and enhanced NH3 emissions. The CMAQ model is run for July 2011 using a version of the 2011 National Emissions Inventory in which agricultural NH3 emissions were adjusted to reflect the lower end of the range of estimates from the current process‐based emissions model. The NAQFC CMAQ model overpredicted atmospheric NH3 at a continuous monitor in Clinton, NC, within the region of intensive animal production. The average concentration measured by the monitor was 6.6 ppbv, while the average predicted by the model was 10.5 ppbv, a 60% overprediction. Outside of the region of intensive animal production, both measured and modeled NH3 concentrations were low, 1.3 ppbv or less. The model underpredicted wet deposition of NH4+ and dry deposition of NH3. It is believed that the overestimation of NH3 at Clinton is attributable at least in part to the underestimation of wet and dry deposition in North Carolina. Key Points: The National Air Quality Forecast System model overpredicted ammonia at a continuous monitor in a region of intensive animal productionModel predictions for ammonia were improved by using State permit information for locations of animal feeding operationsThe model underpredicted wet deposition of ammonium ion and dry deposition of ammonia in eastern North Carolina [ABSTRACT FROM AUTHOR]

Ambient concentrations of both fine particulate matter (PM2.5) and particulate matter with an aerodynamic diameter less than 10 micron (PM10) were measured from 10 June 2015 to 13 July 2015 at three locations surrounding the Cheswick Power Plant, which is located between the boroughs of Springdale and Cheswick, Pennsylvania. The average concentrations of PM10 observed during the periods were 20.5 ± 10.2 μg m-3 (Station 1), 16.1 ± 4.9 μg m-3 (Station 2) and 16.5 ± 7.1 μg m-3 (Station 3). The average concentrations of PM2.5 observed at the stations were 9.1 ± 5.1 μg m-3 (Station 1), 0.2 ± 0.4 μg m-3 (Station 2) and 11.6 ± 4.8 μg m-3 (Station 3). In addition, concentrations of PM2.5 measured by four Pennsylvania Department of Environmental Protection air quality monitors (all within a radius of 40 miles) were also analyzed. The observed average concentrations at these sites were 12.7 ± 6.9 μg m-3 (Beaver Falls), 11.2 ± 4.7 μg m-3 (Florence), 12.2 ± 5.3 μg m-3 (Greensburg) and 12.2 ± 5.5 μg m-3 (Washington). Elemental analysis for samples (blank - corrected) revealed the presence of metals that are present in coal (i.e., antimony, arsenic, beryllium, cadmium, chromium, cobalt, lead, manganese, mercury, nickel and selenium). [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) were measured near Interstate 40, just east of Research Triangle Park, North Carolina, USA. The goals of this project were to ascertain whether a sufficient quantity of PAHs could be collected using low flow (16.7 L/minute) over 8-h periods and if so, do investigate how the PAHs correlate to local sources, atmospheric pollutants and meteorology. The 8-h integrated samples were collected on 20 sampling days over a two month period during fall 2014. The samples were collected using low flow (BGI Incorporated PQ200) fine particulate samplers analyzed using gas chromatography-mass spectrometry (GC-MS). Temporal distributions of the PAHs (average mean 9.2 nanogram/cubic meter ±9.0 std) were compared to traffic count, and meteorological and pollutant data collected at the near roadway station. Using the meteorological data (i.e., wind speed and direction vector data), wind roses were created illustrating the local sources of the PAHs. In terms of correlation to atmospheric oxidants, (i.e., ozone, nitrogen dioxide and nitric oxide) wind rose analysis illustrated the morning hours which were predominantly southern winds, while the afternoon hours illustrated southerly and easterly winds, which suggests that the automobile traffic is the main source of PAHs. The nighttime hours wind rose shows winds from the northerly and easterly direction, which are predominantly from the RDU International Airport. Since the wind direction vectors illustrated that the afternoon hours (i.e., 12 p.m. to 8 p.m.) were from the interstate, comparisons were performed on the samples collected in this time period for both the traffic and pollutant data. The comparison of the traffic data showed a correlation with the number of vehicles (>60 feet i.e., heavy duty diesel engine vehicles). In addition, with the ozone, nitrogen dioxide and nitric oxide) there is a significant linear correlation between the sum of the measured PAHs with nitric oxide (NO), nitrogen dioxide (NO2) and ozone (O³) with the R² values being 0.1, 0.04 and 0.07 respectively. An analysis of variance (ANOVA) statistical regression was performed on the pollutant data versus the measured sum of the PAHs. With the alpha set at 0.05, (α = 0.05) the p-values for O3, NO2 and NO were 0.00613, 0.000496 and 0.000264, respectively, which are significant. In addition, the PAH concentration found in this study compare favorably to other published studies (0.1 to 193.6 ng/m³) both nationally and internationally. [ABSTRACT FROM AUTHOR]

Reduced sulfur compounds (RSCs) emissions from concentrated animal feeding operations (CAFOs) have become a potential environmental and human health concern, as a result of changes in livestock production methods. RSC emissions were determined from a swine CAFO in North Carolina. RSC measurements were made over a period of ~1 week from both the barn and lagoon during each of the four seasonal periods from June 2007 to April 2008. During sampling, meteorological and other environmental parameters were measured continuously. Seasonal hydrogen sulfide (H2S) barn concentrations ranged from 72 to 631 ppb. Seasonal dimethyl sulfide (DMS; CH3SCH3) and dimethyl disulfide (DMDS; CH3S2CH3) concentrations were 2-3 orders of magnitude lower, ranging from 0.18 to 0.89 ppb and 0.47 to 1.02 ppb, respectively. The overall average barn emission rate was 3.3 g day-1 AU-1 (AU (animal unit) = 500 kg of live animal weight) for H2S, which was approximately two orders of magnitude higher than the DMS and DMDS overall average emissions rates, determined as 0.017 g day-1 AU-1 and 0.036 g day-1 AU-1, respectively. The overall average lagoon flux was 1.33 µg m-2 min-1 for H2S, which was approximately an order of magnitude higher than the overall average DMS (0.12 µg m-2 min-1) and DMDS (0.09 µg m-2 min-1) lagoon fluxes. The overall average lagoon emission for H2S (0.038 g day-1 AU-1) was also approximately an order of magnitude higher than the overall average DMS (0.0034 g day-1 AU-1) and DMDS (0.0028 g day-1 AU-1) emissions. H2S, DMS and DMDS have offensive odors and low odor thresholds. Over all four sampling seasons, 77% of 15 min averaged H2S barn concentrations were an order of magnitude above the average odor threshold. During these sampling periods, however, DMS and DMDS concentrations did not exceed their odor thresholds. The overall average barn and lagoon emissions from this study were used to help estimate barn, lagoon and total (barn + lagoon) RSC emissions from swine CAFOs in North Carolina. Total (barn + lagoon) H2S emissions from swine CAFOs in North Carolina were estimated to be 1.22*106 kg yr-1. The barns had significantly higher H2S emissions than the lagoons, contributing ~98% of total North Carolina H2S swine CAFO emissions. Total (barn + lagoon) emissions for DMS and DMDS were 1-2 orders of magnitude lower, with barns contributing ~86% and ~93% of total emissions, respectively. H2S swine CAFO emissions were estimated to contribute ~18% of North Carolina H2S emissions. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates the environmental exposure of residents of a community in southwest Virginia to respirable concentrations of dust (PM-10 i.e. PM10) generated by trucks hauling coal from surface coal mining operations. The study site is representative of communities in southwest Virginia and other parts of Appalachia that are located in narrow hollows where homes are placed directly along roads that experience heavy coal truck traffic. Preliminary air sampling (Particulate Matter i.e. PM10) was conducted for a period of approximately two weeks during early August 2008 in the unincorporated community of Roda, Virginia, at two locations (about a mile apart along Roda Road (Route 685) in Wise County, Virginia). For the purposes of this study (a combination of logistics, resource, and characterization of PM) we sited the PM samplers near the road to ascertain the micro exposure from the road. The results revealed high levels of PM10 (the mean adjusted 24-h concentration at the Campbell Site=250.2μgm−3 (±135.0μgm−3); and at the Willis Site=144.8±60.0μgm−3). The U.S. 24-h national ambient air quality standard for PM10 is 150μgm−3. Elemental analysis for samples (blank-corrected) collected on Quartz filter paper (on one randomly selected day) at both the sites revealed the presence of antimony, arsenic, beryllium, cadmium, chromium, cobalt, lead, manganese, mercury, nickel, selenium. Electron micrographs reveal the morphology and habit (shapes and aggregates) of the particulate matter collected. [Copyright &y& Elsevier]

Two case studies are performed to improve ammonia emissions inputs used to model fine particulate matter (PM2.5 is the portion of particulate matter smaller than 2.5 μm aerodynamic diameter) formation of ammonium sulfate and ammonium nitrate. Ammonia emissions are analyzed in detail for North Carolina and the San Joaquin Valley (SJV) of California, with a focus on the Charlotte, NC, and Fresno, California metropolitan areas. A new gridded ammonia emissions inventories suitable for atmospheric modeling for the two case study cities was also developed.Agricultural sources accounted for the bulk of ammonia emissions in both case studies. Livestock waste contributed about 80% in North Carolina and 64% in the SJV, while fertilizer application contributed about 6–7% in both domains. Forests and non-agricultural vegetation contributed 5% in North Carolina and 12% in the SJV. Motor vehicles accounted for about 6% of ammonia emissions in North Carolina and 14% in the SJV. In the Charlotte and Fresno urban areas, the distribution of emissions is less heavily weighted toward agricultural sources and more heavily weighted toward highway vehicles (highway vehicles account for an estimated 64% of emissions in Charlotte and 51% of emissions in Fresno). The emissions estimates for agricultural sources (livestock and fertilizer application) decline to approximately 14% in the winter for both the Charlotte and Fresno urban areas. Emissions estimates for soils and vegetation also decline to approximately 0 during the winter for both the Fresno and Charlotte area. As a result, motor vehicles account for a larger fraction (approximately 73% and 70% for Charlotte and Fresno, respectively) of winter ammonia emissions, particularly in the Charlotte urban area. [Copyright &y& Elsevier]

Atmospheric ammonia (NH 3 ) is not only a major precursor gas for fine particulate matter (PM 2.5 ), but it also negatively impacts the environment through eutrophication and acidification. As the need for agriculture, the largest contributing source of NH 3 , increases, NH 3 emissions will also increase. Therefore, it is crucial to accurately predict ammonia concentrations. The objective of this study is to determine how well the U.S. National Oceanic and Atmospheric Administration (NOAA) National Air Quality Forecast Capability (NAQFC) system predicts ammonia concentrations using their Community Multiscale Air Quality (CMAQ) model (v4.6). Model predictions of atmospheric ammonia are compared against measurements taken during the NOAA California Nexus (CalNex) field campaign that took place between May and July of 2010. Additionally, the model predictions were also compared against ammonia measurements obtained from the Tropospheric Emission Spectrometer (TES) on the Aura satellite. The results of this study showed that the CMAQ model tended to under predict concentrations of NH 3 . When comparing the CMAQ model with the CalNex measurements, the model under predicted NH 3 by a factor of 2.4 (NMB = −58%). However, the ratio of the median measured NH 3 concentration to the median of the modeled NH 3 concentration was 0.8. When compared with the TES measurements, the model under predicted concentrations of NH 3 by a factor of 4.5 (NMB = −77%), with a ratio of the median retrieved NH 3 concentration to the median of the modeled NH 3 concentration of 3.1. Because the model was the least accurate over agricultural regions, it is likely that the major source of error lies within the agricultural emissions in the National Emissions Inventory. In addition to this, the lack of the use of bidirectional exchange of NH 3 in the model could also contribute to the observed bias. [ABSTRACT FROM AUTHOR]

Accurate estimates of ammonia (NH3) emissions are needed for reliable predictions of fine particulate matter (PM2.5) by air quality models (AQMs), but the current estimates contain large uncertainties in the temporal and spatial distributions of NH3 emissions. In this study, the US EPA Community Multiscale Air Quality (CMAQ) modeling system is applied to study the contributions of the agriculture–livestock NH3 (AL-NH3) emissions to the concentration of PM2.5 and the uncertainties in the total amount and the temporal variations of NH3 emissions and their impact on the formation of PM2.5 for August and December 2002. The sensitivity simulation results show that AL-NH3 emissions contribute significantly to the concentration of PM2.5, NH4 +, and NO3 −; their contributions to the concentrations of SO4 2− are relatively small. The impact of NH3 emissions on PM2.5 formation shows strong spatial and seasonal variations associated with the meteorological conditions and the ambient chemical conditions. Increases in NH3 emissions in August 2002 resulted in >10% increases in the concentrations of NH4 + and NO3 −; reductions in NH3 emissions in December 2002 resulted in >20% decreases in their concentrations. The large changes in species concentrations occur downwind of the high NH3 emissions where the ambient environment is NH3-poor or neutral. The adjustments in NH3 emissions improve appreciably the model predictions of NH4 + and NO3 − both in August and December, but resulted in negligible improvements in PM2.5 in August and a small improvement in December, indicating that other factors (e.g., inaccuracies in meteorological predictions, emissions of other primary species, aerosol treatments) might be responsible for model biases in PM2.5. [Copyright &y& Elsevier]

The atmospheric transport and fate of ammonia (NH3) depend on both meteorological and chemical conditions once it is emitted into the atmosphere. The largest source contributing to NH3 emission is the agricultural production, in particular animal operation, in North Carolina (NC). In this study, three-dimensional numerical meteorological and air quality models are applied to study the transport and fate of NH3 in the atmosphere in an area in southeast US centered over NC. One summer and one winter month simulations with a 4-km horizontal grid were conducted to simulate the meteorological and chemical environments for the transport and transformation of the reduced nitrogen, NH x (=NH3+NH4 +) and to examine its seasonal variations and interactions with other chemical species (e.g., ozone and fine particular matter, PM2.5). The model performance for simulated meteorology and air quality was evaluated against observations in terms of spatial distributions, temporal variations, and statistical trends. MM5/CMAQ gave an overall good performance for meteorological variables and O3 mixing ratios and a reasonably good performance for PM2.5. The simulations show that 10–40% of total NH3 was converted to NH4 + at/near source and 40–100% downwind in August, and the conversion rates were 20–50% at/near source and 50–98% downwind in December. While the 3-D atmospheric models demonstrate some skills in capturing synoptic meteorological patterns, diurnal variations of concentrations of oxidants and PM2.5, and regional transport and transformation of NH x , reproducing meteorological and chemical features at a local scale and the magnitudes of hourly concentrations of oxidants and PM2.5 remain challenging due to uncertainties in model inputs and treatments. [Copyright &y& Elsevier]