Overview of surface measurements and spatial characterization of submicrometer particulate matter during the DISCOVER-AQ 2013 campaign in Houston,TX

The sources of submicrometer particulate matter (PM₁) remain poorly characterized in the industrialized city of Houston, TX. A mobile sampling approach was used to characterize PM₁ composition and concentration across Houston based on high-time-resolution measurements of nonrefractory PM₁ and trace gases during the DISCOVER-AQ Texas 2013 campaign. Two pollution zones with marked differences in PM₁ levels, character, and dynamics were established based on cluster analysis of organic aerosol mass loadings sampled at 16 sites. The highest PM₁ mass concentrations (average 11.6 ± 5.7 µg/m³) were observed to the northwest of Houston (zone 1), dominated by secondary organic aerosol (SOA) mass likely driven by nighttime biogenic organonitrate formation. Zone 2, an industrial/urban area south/east of Houston, exhibited lower concentrations of PM₁ (average 4.4 ± 3.3 µg/m³), significant organic aerosol (OA) aging, and evidence of primary sulfate emissions. Diurnal patterns and backward-trajectory analyses enable the classification of airmass clusters characterized by distinct PM sources: biogenic SOA, photochemical aged SOA, and primary sulfate emissions from the Houston Ship Channel. Principal component analysis (PCA) indicates that secondary biogenic organonitrates primarily related with monoterpenes are predominant in zone 1 (accounting for 34% of the variability in the data set). The relevance of photochemical processes and industrial and traffic emission sources in zone 2 also is highlighted by PCA, which identifies three factors related with these processes/sources (~50% of the aerosol/trace gas concentration variability). PCA reveals a relatively minor contribution of isoprene to SOA formation in zone 1 and the absence of isoprene-derived aerosol in zone 2. The relevance of industrial amine emissions and the likely contribution of chloride-displaced sea salt aerosol to the observed variability in pollution levels in zone 2 also are captured by PCA.

Implications: This article describes an urban-scale mobile study to characterize spatial variations in submicrometer particulate matter (PM₁) in greater Houston. The data set indicates substantial spatial variations in PM₁ sources/chemistry and elucidates the importance of photochemistry and nighttime oxidant chemistry in producing secondary PM₁. These results emphasize the potential benefits of effective control strategies throughout the region, not only to reduce primary emissions of PM₁ from automobiles and industry but also to reduce the emissions of important secondary PM₁ precursors, including sulfur oxides, nitrogen oxides, ammonia, and volatile organic compounds. Such efforts also could aid in efforts to reduce mixing ratios of ozone.     

Probability analyses of combining background concentrations with model-predicted concentrations

In order to calculate total concentrations for comparison to ambient air quality standards, monitored background concentrations are often combined with model predicted concentrations. Models have low skill in predicting the locations or time series of observed concentrations. Further, adding fixed points on the probability distributions of monitored and predicted concentrations is very conservative and not mathematically correct. Simply adding the 99th percentile predicted to the 99th percentile background will not yield the 99th percentile of the combined distributions. Instead, an appropriate distribution can be created by calculating all possible pairwise combinations of the 1-hr daily maximum observed background and daily maximum predicted concentration, from which a 99th percentile total value can be obtained. This paper reviews some techniques commonly used for determining background concentrations and combining modeled and background concentrations. The paper proposes an approach to determine the joint probabilities of occurrence of modeled and background concentrations. The pairwise combinations approach yields a more realistic prediction of total concentrations than the U.S. Environmental Protection Agency's (EPA) guidance approach and agrees with the probabilistic form of the National Ambient Air Quality Standards.

Implications: EPA's current approaches to determining background concentrations for compliance modeling purposes often lead to “double counting” of background concentrations and actual plume impacts and thus lead to overpredictions of total impacts. Further, the current Tier 1 approach of simply adding the top ends of the background and model predicted concentrations (e.g., adding the 99th percentiles of these distributions together) results in design value concentrations at probabilities in excess of the form of the National Ambient Air Quality Standards.     

Major ionic compositions of fine particulate matter in an animal feeding operation facility and its vicinity

Animal feeding operations (AFOs) produce particulate matter (PM) and gaseous pollutants. Investigation of the chemical composition of PM_2.5 inside and in the local vicinity of AFOs can help to understand the impact of the AFO emissions on ambient secondary PM formation. This study was conducted on a commercial egg production farm in North Carolina. Samples of PM_2.5 were collected from five stations, with one located in an egg production house and the other four located in the vicinity of the farm along four wind directions. The major ions of NH₄⁺, Na⁺, K⁺, SO₄^2?, Cl^?, and NO₃^? were analyzed using ion chromatography (IC). In the house, the mostly abundant ions were SO₄^2?, Cl^?, and K⁺. At ambient stations, SO₄^2?, and NH₄⁺ were the two most abundant ions. In the house, NH₄⁺, SO₄^2?, and NO₃^? accounted for only 10% of the PM_2.5 mass; at ambient locations, NH₄⁺, SO₄^2?, and NO₃^? accounted for 36–41% of the PM_2.5 mass. In the house, NH₄⁺ had small seasonal variations indicating that gas-phase NH₃ was not the only major force driving its gas–particle partitioning. At the ambient stations, NH₄⁺ had the highest concentrations in summer. In the house, K⁺, Na⁺, and Cl^? were highly correlated with each other. In ambient locations, SO₄^2? and NH₄⁺ had a strong correlation, whereas in the house, SO₄^2? and NH₄⁺ had a very weak correlation. Ambient temperature and solar radiation were positively correlated with NH₄⁺ and SO₄^2?. This study suggests that secondary PM formation inside the animal house was not an important source of PM_2.5. In the vicinity, NH₃ emissions had greater impact on PM_2.5 formation.

ImplicationsThe chemical composition of PM_2.5 inside and in the local vicinity of AFOs showed the impact of the AFO emissions on ambient secondary PM_2.5 formation, and the fate and transport of air pollutants associated with AFOs. The results may help to manage in-house animal facility air quality, and to develop regional air quality control strategies and policies, especially in animal agriculture-concentrated areas.     

Comparative statistical study of hourly precipitation determined by radar-based Stage IV and ground-based methods in the North Central United States

Detailed hourly precipitation data are required for long-range modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants using the CALPUFF model. In sparsely populated areas such as the north central United States, ground-based precipitation measurement stations may be too widely spaced to offer a complete and accurate spatial representation of hourly precipitation within a modeling domain. The availability of remotely sensed precipitation data by satellite and the National Weather Service array of next-generation radars (NEXRAD) deployed nationally provide an opportunity to improve on the paucity of data for these areas. Before adopting a new method of precipitation estimation in a modeling protocol, it should be compared with the ground-based precipitation measurements, which are currently relied upon for modeling purposes. This paper presents a statistical comparison between hourly precipitation measurements for the years 2006 through 2008 at 25 ground-based stations in the north central United States and radar-based precipitation measurements available from the National Center for Environmental Predictions (NCEP) as Stage IV data at the nearest grid cell to each selected precipitation station. It was found that the statistical agreement between the two methods depends strongly on whether the ground-based hourly precipitation is measured to within 0.1 in/hr or to within 0.01 in/hr. The results of the statistical comparison indicate that it would be more accurate to use gridded Stage IV precipitation data in a gridded dispersion model for a long-range simulation, than to rely on precipitation data interpolated between widely scattered rain gauges.

Implications:

The current reliance on ground-based rain gauges for precipitation events and hourly data for modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants results in potentially large discontinuity in data coverage and the need to extrapolate data between monitoring stations. The use of radar-based precipitation data, which is available for the entire continental United States and nearby areas, would resolve these data gaps and provide a complete and accurate spatial representation of hourly precipitation within a large modeling domain.     

U.S. National PM2.5 Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

The U.S. Environmental Protection Agency (EPA) initiated the national PM_2.5 Chemical Speciation Monitoring Network (CSN) in 2000 to support evaluation of long-term trends and to better quantify the impact of sources on particulate matter (PM) concentrations in the size range below 2.5 μm aerodynamic diameter (PM_2.5; fine particles). The network peaked at more than 260 sites in 2005. In response to the 1999 Regional Haze Rule and the need to better understand the regional transport of PM, EPA also augmented the long-existing Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility monitoring network in 2000, adding nearly 100 additional IMPROVE sites in rural Class 1 Areas across the country. Both networks measure the major chemical components of PM_2.5 using historically accepted filter-based methods. Components measured by both networks include major anions, carbonaceous material, and a series of trace elements. CSN also measures ammonium and other cations directly, whereas IMPROVE estimates ammonium assuming complete neutralization of the measured sulfate and nitrate. IMPROVE also measures chloride and nitrite. In general, the field and laboratory approaches used in the two networks are similar; however, there are numerous, often subtle differences in sampling and chemical analysis methods, shipping, and quality control practices. These could potentially affect merging the two data sets when used to understand better the impact of sources on PM concentrations and the regional nature and long-range transport of PM_2.5. This paper describes, for the first time in the peer-reviewed literature, these networks as they have existed since 2000, outlines differences in field and laboratory approaches, provides a summary of the analytical parameters that address data uncertainty, and summarizes major network changes since the inception of CSN.

ImplicationsTwo long-term chemical speciation particle monitoring networks have operated simultaneously in the United States since 2001, when the EPA began regular operations of its PM_2.5 Chemical Speciation Monitoring Network (IMPROVE began in 1988). These networks use similar field sampling and analytical methods, but there are numerous, often subtle differences in equipment and methodologies that can affect the results. This paper describes these networks since 2000 (inception of CSN) and their differences, and summarizes the analytical parameters that address data uncertainty, providing researchers and policymakers with background information they may need (e.g., for 2018 PM_2.5 designation and State Implementation Plan process; McCarthy, 2013) to assess results from each network and decide how these data sets can be mutually employed for enhanced analyses. Changes in CSN and IMPROVE that have occurred over the years also are described.     

Bioremediation assessment of diesel–biodiesel-contaminated soil using an alternative bioaugmentation strategy

This study investigated the effectiveness of successive bioaugmentation, conventional bioaugmentation, and biostimulation of biodegradation of B10 in soil. In addition, the structure of the soil microbial community was assessed by polymerase chain reaction-denaturing gradient gel electrophoresis. The consortium was inoculated on the initial and the 11th day of incubation for successive bioaugmentation and only on the initial day for bioaugmentation and conventional bioaugmentation. The experiment was conducted for 32 days. The microbial consortium was identified based on sequencing of 16S rRNA gene and consisted as Pseudomonas aeruginosa, Achromobacter xylosoxidans, and Ochrobactrum intermedium. Nutrient introduction (biostimulation) promoted a positive effect on microbial populations. The results indicate that the edaphic community structure and dynamics were different according to the treatments employed. CO₂ evolution demonstrated no significant difference in soil microbial activity between biostimulation and bioaugmentation treatments. The total petroleum hydrocarbon (TPH) analysis indicated a biodegradation level of 35.7 and 32.2 % for the biostimulation and successive bioaugmentation treatments, respectively. Successive bioaugmentation displayed positive effects on biodegradation, with a substantial reduction in TPH levels.     

Estimated crop yield losses due to surface ozone exposure and economic damage in India

In this study, we estimate yield losses and economic damage of two major crops (winter wheat and rabi rice) due to surface ozone (O₃) exposure using hourly O₃ concentrations for the period 2002–2007 in India. This study estimates crop yield losses according to two indices of O₃ exposure: 7-h seasonal daytime (0900–1600 hours) mean measured O₃ concentration (M7) and AOT40 (accumulation exposure of O₃ concentration over a threshold of 40 parts per billion by volume during daylight hours (0700–1800 hours), established by field studies. Our results indicate that relative yield loss from 5 to 11 % (6–30 %) for winter wheat and 3–6 % (9–16 %) for rabi rice using M7 (AOT40) index of the mean total winter wheat 81 million metric tons (Mt) and rabi rice 12 Mt production per year for the period 2002–2007. The estimated mean crop production loss (CPL) for winter wheat are from 9 to 29 Mt, account for economic cost loss was from 1,222 to 4,091 million US$ annually. Similarly, the mean CPL for rabi rice are from 0.64 to 2.1 Mt, worth 86–276 million US$. Our calculated winter wheat and rabi rice losses agree well with previous results, providing the further evidence that large crop yield losses occurring in India due to current O₃ concentration and further elevated O₃ concentration in future may pose threat to food security.     

The effect of aerosol optical depth on rainfall with reference to meteorology over metro cities in India

Rainfall is a key link in the global water cycle and a proxy for changing climate; therefore, proper assessment of the urban environment’s impact on rainfall will be increasingly important in ongoing climate diagnostics and prediction. Aerosol optical depth (AOD) measurements on the monsoon seasons of the years 2008 to 2010 were made over four metro regional hotspots in India. The highest average of AOD was in the months of June and July for the four cities during 3 years and lowest was in September. Comparing the four regions, Kolkata was in the peak of aerosol contamination and Chennai was in least. Pearson correlation was made between AOD with climatic parameters. Some changes in the parameters were found during drought year. Temperature, cloud parameters, and humidity play an important role for the drought conditions. The role of aerosols, meteorological parameters, and their impacts towards the precipitation during the monsoon was studied.     

A new bioseed for determination of wastewater biodegradability: analysis of the experimental procedure

A new bioassay proposed in the patent P201300029 was applied to a pre-treated wastewater containing a mixture of commercial pesticides to simulate a recalcitrant industrial wastewater in order to determine its biodegradability. The test uses a mixture of standardized inoculum of the lyophilized bacteria Pseudomonas putida with the proper proportion of salts and minerals. The results highlight that biodegradation efficiency can be calculated using a gross parameter (chemical oxygen demand (COD)) which facilitates the biodegradability determination for routine water biodegradability analysis. The same trend was observed throughout the assay with the dehydrated and fresh inoculums, and only a difference of 5 % in biodegradation efficiency (E _f) was observed. The obtained results showed that the P. putida biodegradability assay can be used as a commercial test with a lyophilized inoculum in order to monitor the ready biodegradability of an organic pollutant or a WWTP influent. Moreover, a combination of the BOD₅/COD ratio and the P. putida biodegradability test is an attractive alternative in order to evaluate the biodegradability enhancement in water pre-treated with advanced oxidation processes (AOPs).