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.     

Comparison of analytical descriptions of nocturnal low-level jets within the Ekman model framework

This study focuses on the inertial oscillation aspect of the nocturnal low-level jet (NLLJ). In the context of the Ekman model solutions, conceptual NLLJ inertial oscillation analytical frameworks proposed by Blackadar in 1957 and Shapiro and Fedorovich and van de Wiel et al. in 2010 are compared. Considering a NLLJ produced via direct numerical simulation over flat terrain with no baroclinic influence as a reference case, the deficiencies of each framework in representing a realistic NLLJ are assessed. The Blackadar theory results in unrealistic wind profiles near the surface. While extensions of Blackadar’s framework by Shapiro and Fedorovich and van de Wiel et al. produce more realistic NLLJs, the simpler approach taken by van de Wiel et al. does not describe the NLLJ wind hodograph at later times sufficiently in qualitative terms.     

Automated riverine landscape characterization: GIS-based tools for watershed-scale research, assessment, and management

River systems consist of hydrogeomorphic patches (HPs) that emerge at multiple spatiotemporal scales. Functional process zones (FPZs) are HPs that exist at the river valley scale and are important strata for framing whole-watershed research questions and management plans. Hierarchical classification procedures aid in HP identification by grouping sections of river based on their hydrogeomorphic character; however, collecting data required for such procedures with field-based methods is often impractical. We developed a set of GIS-based tools that facilitate rapid, low cost riverine landscape characterization and FPZ classification. Our tools, termed RESonate, consist of a custom toolbox designed for ESRI ArcGIS®. RESonate automatically extracts 13 hydrogeomorphic variables from readily available geospatial datasets and datasets derived from modeling procedures. An advanced 2D flood model, FLDPLN, designed for MATLAB® is used to determine valley morphology by systematically flooding river networks. When used in conjunction with other modeling procedures, RESonate and FLDPLN can assess the character of large river networks quickly and at very low costs. Here we describe tool and model functions in addition to their benefits, limitations, and applications.     

A Comparison of Mass,Lead, Sulfate,and Nitrate Concentrations in a Field Study Using Dichotomous,Size-Selective,and Standard Hi-Vol Samplers

Air monitoring In the San Francisco Bay Area was carried out to measure outdoor community air concentrations of poly cyclic aromatic hydrocarbons (PAH) and mutagenlc activity (mutagenlclty) In participate organic matter (POM). Monitoring began In 1979 and Is currently conducted at six stations. PAH and mutagenlclty tests were performed on organic extracts prepared from high volume (hl-vol) filters composited every four months, by meteorological season. PAH were determined by high pressure liquid chromatography (HPLC) with fluorescence and ultraviolet detection. Mutagenlclty was measured In the Ames Salmonella bloas-say using strain TA98 with and without metabolic activation. The nine-year mean concentration of benzo(a)pyrene (BaP) was 0.4 ng/m³. The mutagenlcfty of this amount of BaP accounted for only about 0.2% of the observed mutagenicity In POM and other measured PAH accounted for even less. Concentrations of PAH and mutagenlclty were three to nine times higher during the winter than during other seasons. Year-to-year wintertime trends In several PAH were also seen. Early In the 1980s, winter concentrations of BaP and benzo (g,h,i)perylene Increased. However since the mld-1980’s, their concentrations have fallen. The decrease In PAH concentrations may be the result of an Increasing proportion of vehicles with relatively low organic emissions. In contrast to PAH, mutagenlcfty did not show significant year-to-year time trends.     

Brassica napus hairy roots and rhizobacteria for phenolic compounds removal

Phenolic compounds are contaminants frequently found in water and soils. In the last years, some technologies such as phytoremediation have emerged to remediate contaminated sites. Plants alone are unable to completely degrade some pollutants; therefore, their association with rhizospheric bacteria has been proposed to increase phytoremediation potential, an approach called rhizoremediation. In this work, the ability of two rhizobacteria, Burkholderia kururiensis KP 23 and Agrobacterium rhizogenes LBA 9402, to tolerate and degrade phenolic compounds was evaluated. Both microorganisms were capable of tolerating high concentrations of phenol, 2,4-dichlorophenol (2,4-DCP), guaiacol, or pentachlorophenol (PCP), and degrading different concentrations of phenol and 2,4-DCP. Association of these bacterial strains with B. napus hairy roots, as model plant system, showed that the presence of both rhizospheric microorganisms, along with B. napus hairy roots, enhanced phenol degradation compared to B. napus hairy roots alone. These findings are interesting for future applications of these strains in phenol rhizoremediation processes, with whole plants, providing an efficient, economic, and sustainable remediation technology.     

Emissions into the Atmosphere Due to the Ceramic Salt Glazing Process

The techniques of Principal Component Analysis (PCA) and subsequent regression analysis were used in an attempt to describe local and upwind chemical and physical factors which affect the variability of SO₄ ^–2 concentrations observed in a rural area of the northeastern U.S. The data used in the analyses included upwind and local O₃ concentrations, temperature, relative humidity and other climatological information, SO₂, and meteorological information associated with backward trajectories. The investigation identified five principal components, three major (eigenvalues >1) and two minor (eigenvalues < one), which accounted for 52% (r = 0.72) of the variability in the SO₄ ^–2 regression model. These components can be described as representing local and upwind photochemistry, droplet growth, SO₂ emissions, and air mass characteristics. The study also indicated that in future studies it will be necessary to a priori select air pollution and meteorological variables for measurement to potentially increase the sensitivity of this type of receptor model.     

Modeling the impacts of calcite precipitation on the epilimnion of an ultraoligotrophic, hard-water lake

A mass-balance model of calcite precipitation was developed to investigate the interactions of the varied processes governing the generation and fate of calcite in lakes. The model was used in conjunction with data to assess the evolution and impact of calcite precipitation for calcareous, ultraoligotrophic Torch Lake, Michigan (USA). This lake is an ideal setting for implementation of a baseline modeling study of calcite precipitation where the physical drivers could be evaluated without being dominated, as in many systems, by biological processes. The model provides a representation of calcite precipitation with particulate surface area changing over time, and demonstrates that it is possible for the change in water clarity to be explained by calcite precipitation employing standard optical models. Using the mass balance model to quantify the roles of the various chemical, biological and physical processes interacting in the lake's epilimnion, it was shown that the seasonal temperature rise and air-water CO₂ exchange drive calcite precipitation much more than primary production for this ultraoligotrophic system.     

The impact of an industrial complex on freshly deposited sediments, Chener Rahdar river case study, Shiraz, Iran

Concentrations of elements (As, Co, Cu, Ni, Mo, Pb, V, and Zn) are studied in the sediments of two adjacent stretches of Chenar Rahdar river. The first stretch (S1) is influenced by urban and arable land wastewater, and the second (S2) is mainly loaded with industrial effluents. The average abundance order of heavy metals content in S1 sediments is Ni > V > Zn > Cu > Co > As > Pb > Mo and in S2 sediments is Ni > Zn > V > Cu > Mo > Pb > Co > As. The maximum average concentration for these heavy metals (except for As) occurs in the S2 sediments. The contamination factor (CF) base of background in S1 for eight analyzed elements is moderate. The CF for Cu, Zn, and Pb in S2 sediments is considerable. The highest CF in S1 and S2 sediments is observed for Mo (CF = 10.95 and 12.41) and indicates very high contamination. The application of modified degree of contamination values (mC_d) indicates low and high degree of contamination (1.89–4.15) in S1 and S2, respectively. Calculated enrichment factors (EF) reveal enrichment of Mo and As in S1 and Zn, Cu, Mo, and Pb in S2 compared to the average abundances of background level. The maximum EF for Mo is 7.61 (significant enrichment), while Pb, Zn, and Cu with maximum EF between 2 and 5 indicate moderate contamination. Principal component analysis (PCA) shows distinctly different elemental associations in S1 and S2 sediments. The strong association of Zn, Co, Ni, Sc, Cu, Al and Fe in S1 suggests a similar source. The results of PCA for Zn, Pb, Mo and Cu in S2 (componente2) indicate that these metals are influenced by anthropogenic activity. Also, high loading heavy metals with OC (0.97) indicate that organic carbon plays a significant role in the distribution and sorption of these heavy metals in the sediments. Factor analysis indicates that As and Mo behave differently in sediment samples.     

Potential Impact of Clean Air Act Regulations on Nitrogen Fate and Transport in the Neuse River Basin: a Modeling Investigation Using CMAQ and SWAT

There has been extensive analysis of Clean Air Act Amendment (CAAA) regulation impacts to changes in atmospheric nitrogen deposition; however, few studies have focused on watershed nitrogen transfer particularly regarding long-term predictions. In this study, we investigated impacts of CAAA NOx emissions on the fate and transport of nitrogen for two watersheds in the Neuse River Basin. We applied the Soil and Water Assessment Tool (SWAT) using simulated deposition rates from the Community Multiscale Air Quality (CMAQ) model. Two scenarios were investigated: one that considered CAAA emission controls in CMAQ simulation (with) and a second that did not (without). By 2020, results showed a 70 % drop in nitrogen discharge for the Little River watershed and a 50 % drop for the Nahunta watershed from 1990 levels under the with-CAAA scenario. Denitrification and plant nitrogen uptake played important roles in nitrogen discharge from each watershed. Nitrogen watershed response time to a change in atmospheric nitrogen deposition was 4 years for Nahunta and 2 years for Little River. We attribute these differences in nitrogen response time to contrasts in agricultural land use and diversity of crop types. Soybean, hay, and corn land covers had comparatively longer response times to changes in atmospheric deposition. The studied watersheds demonstrate relatively large nitrogen retention: ≥80 % of all delivered nitrogen.