Ammonia in the atmosphere: a review on emission sources, atmospheric chemistry and deposition on terrestrial bodies

Gaseous ammonia (NH₃) is the most abundant alkaline gas in the atmosphere. In addition, it is a major component of total reactive nitrogen. The largest source of NH₃ emissions is agriculture, including animal husbandry and NH₃-based fertilizer applications. Other sources of NH₃ include industrial processes, vehicular emissions and volatilization from soils and oceans. Recent studies have indicated that NH₃ emissions have been increasing over the last few decades on a global scale. This is a concern because NH₃ plays a significant role in the formation of atmospheric particulate matter, visibility degradation and atmospheric deposition of nitrogen to sensitive ecosystems. Thus, the increase in NH₃ emissions negatively influences environmental and public health as well as climate change. For these reasons, it is important to have a clear understanding of the sources, deposition and atmospheric behaviour of NH₃. Over the last two decades, a number of research papers have addressed pertinent issues related to NH₃ emissions into the atmosphere at global, regional and local scales. This review article integrates the knowledge available on atmospheric NH₃ from the literature in a systematic manner, describes the environmental implications of unabated NH₃ emissions and provides a scientific basis for developing effective control strategies for NH₃.     

Water Quality and Land Use Changes in the Alafia and Hillsborough River Watersheds,Florida, USA1

Abstract: Spatial distribution of land use can have a substantial effect on surface and groundwater quality. Our objective was to test for trends in flow components and water quality related to changes in land use in the Alafia and Hillsborough River watersheds in Florida, USA, over the period 1974‐2007. In addition, water quality statistics were evaluated in the perspective of numeric water quality criteria and proposed reclassification of segments of the Alafia River. Trends in 10 water quality parameters and three discharge variables were evaluated using a nonparametric trend detection test. Results of land use analysis indicated substantial urbanization and loss of agricultural land in the study area. Discharge variables did not exhibit significant trends, whereas trends in the majority of water quality concentrations were negative or nonsignificant with total nitrogen and total Kjeldahl nitrogen as exceptions showing positive trends. Changes in nutrient pathways could not be clearly identified. Considering recently promulgated numeric nutrient criteria and standards for dissolved fluoride, much of the Alafia River was found to be out of compliance. While there were land use changes and changes in water quality over the study period, it was difficult to identify a direct cause‐effect relationship. Responses to regulatory efforts, such as the Clean Water Act and improvements in phosphate mining practices, may have had greater impacts on water quality than changes in land use.     

Co-pyrolysis of sewage sludge and biomass for stabilizing heavy metals and reducing biochar toxicity: A review

The huge amounts of sewage sludge produced by municipal wastewater treatment plants induce major environmental and economical issues, calling for advanced disposal methods. Traditional methods for sewage sludge disposal increase greenhouse gas emissions and pollution. Moreover, biochar created from sewage sludge often cannot be used directly in soil applications due to elevated levels of heavy metals and other toxic compounds, which alter soil biota and earthworms. This has limited the application of sewage sludge-derived biochar as a fertilizer. Here, we review biomass and sewage sludge co-pyrolysis with a focus on the stabilization of heavy metals and toxicity reduction of the sludge-derived biochar. We observed that co-pyrolyzing sewage sludge with biomass materials reduced heavy metal concentrations and decreased the environmental risk of sludge-derived biochar by up to 93%. Biochar produced from sewage sludge and biomass co-pyrolysis could enhance the reproduction stimulation of soil biota by 20‒98%. Heavy metals immobilization and transformation are controlled by the co-feed material mixing ratio, pyrolysis temperature, and pyrolysis atmosphere.

     

Modeling CO2 exchange and meteorological factors of an apple orchard using partial least square regression

Environmental Science and Pollution Research - The eddy covariance (EC) technique was used to measure variations of orchard-atmosphere CO2 exchange, as a function of meteorological variables in an...     

A methodology for solid waste characterization based on diminishing marginal returns

A methodology is developed for estimating the number of waste sorts for characterizing solid wastes into categories based on diminishing minimum incremental information. Convergence in the square of the coefficient of variation with successive waste sorts is used to indicate cost-efficient termination of sampling at substantially reduced numbers of sorts in comparison with existing methodologies. These findings indicate that the numbers of waste sorts beyond that determined using the proposed methodology do not add substantial marginal gains in information and/or reduction in the confidence interval of the estimate. The methodology is demonstrated using waste composition analyses from the Greater Vancouver Regional District where 22 waste sorts are examined. The proposed methodology is simple, and the number of waste sorts can be estimated with a hand-held calculator and utilized in the field.     

Indoor air quality modeling for PM10, PM2.5, and PM1.0 in naturally ventilated classrooms of an urban Indian school building

Assessment of indoor air quality (IAQ) in classrooms of school buildings is of prime concern due to its potential effects on student??s health and performance as they spend a substantial amount of their time (6?C7 h per day) in schools. A number of airborne contaminants may be present in urban school environment. However, respirable suspended particulate matter (RSPM) is of great significance as they may significantly affect occupants?? health. The objectives of the present study are twofold, one, to measure the concentrations of PM₁₀ (<10  $\upmu $ m), PM_2.5 (<2.5  $\upmu $ m), and PM_1.0 (<1.0  $\upmu $ m) in naturally ventilated classrooms of a school building located near a heavy-traffic roadway (9,755 and 4,296 vehicles/hour during weekdays and weekends, respectively); and second, to develop single compartment mass balance-based IAQ models for PM₁₀ (NVIAQM_pm10), PM_2.5 (NVIAQM_pm2.5), and PM_1.0 (NVIAQM_pm1.0) for predicting their indoor concentrations. Outdoor RSPM levels and classroom characteristics, such as size, occupancy level, temperature, relative humidity, and CO₂ concentrations have also been monitored during school hours. Predicted indoor PM₁₀ concentrations show poor correlations with observed indoor PM₁₀ concentrations (R ² = 0.028 for weekdays, and 0.47 for weekends). However, a fair degree of agreement (d) has been found between observed and predicted concentrations, i.e., 0.42 for weekdays and 0.59 for weekends. Furthermore, NVIAQM_pm2.5 and NVIAQM_pm1.0 results show good correlations with observed concentrations of PM_2.5 (R ² = 0.87 for weekdays and 0.9 for weekends) and PM_1.0 (R ² = 0.86 for weekdays and 0.87 for weekends). NVIAQM_pm10 shows the tendency to underpredict indoor PM₁₀ concentrations during weekdays as it does not take into account the occupant??s activities and its effects on the indoor concentrations during the class hours. Intense occupant??s activities cause resuspension or delayed deposition of PM₁₀. The model results further suggests conductance of experimental and physical simulation studies on dispersion of particulates indoors to investigate their resuspension and settling behavior due to occupant??s activities/movements. The models have been validated at three different classroom locations of the school site. Sensitivity analysis of the models has been performed by varying the values of mixing factor (k) and newly introduced parameter R _c. The results indicate that the change in values of k (0.33 to 1.00) does not significantly affect the model performance. However, change in value of R _c (0.001 to 0.500) significantly affects the model performance.     

Design Variables Associated with Fiberglass/Fly Ash Fabric Filter Cleaning

This paper documents the variation in pressure drop and collection efficiency for fiberglass/fly ash fabric filter systems caused by variations in cleaning intensity (reverse air and reverse pulse), air to cloth ratio, and dust loading. Reverse air rates greater than 2 fpm were required to produce stable pressure characteristics. Pulse jet pressure greater than 50 psi reduced collection efficiency. Increased air to cloth ratios produced decreased collection efficiency.     

Maximum likelihood method for parameter estimation in non-linear models with below detection data

The maximum likelihood (ML) method for regression analyzes of censored data (below detection limit) for nonlinear models is presented. The proposed ML method has been translated into an equivalent least squares method (ML-LS). A two stage iterative algorithm is proposed to estimate statistical parameters from the derived least squares translation. The developed algorithm is applied to a nonlinear model for prediction of ambient air CO concentration in terms of concentrations of respirable particulate matter (RSPM) and NO₂. It has been shown that if censored data are ignored or estimated through simplifications such as (i) censored data are equal to detection limit, (ii) censored data are half of the difference between detection limit and lower limit (e.g., zero or background level) or (iii) censored data are equal to lower limit, this can cause significant bias in estimated parameters. The developed ML-LS method provided better estimates of parameters than any of the simplifications in censored data.     

Organic matter determination for street dust in Delhi

The organic matter of street dust is considered as one of the causes for high human mortality rate. To understand the association, the street dust samples were collected from four different localities (industrial, residential, residential–commercial, and commercial) situated in the greater Delhi area of India. The loss-on-ignition method was used to determine the organic matter (OM) content in street dust. The OM content, potassium, calcium, sulfate, and nitrate concentrations of street dust in Delhi, India is measured to understand the spatial variation. Correlation analysis, analysis of variance, and factor analysis were performed to define the sources. The dust OM level ranges from 2.63 to 10.22 %. It is found through correlation and factor analysis that OM is primarily contributed from secondary aerosol and vehicular exhaust. The OM levels suggest that the use of a residential–commercial site for commercial purposes is polluting the street dust and creating the environmental and human health problems.