Journal of the Air & Waste Management Association最新文献

 The IMPROVE program (Interagency Monitoring of PROtected Visual Environments) tracks long-term trends in the composition and optics of regional haze aerosols in the United States. The absorptance of red (633-nm) light is monitored by filter photometry of 24 h-integrated samples of fine particulate matter (PM_2.5). These are collected every third day at about 150 rural and often remote locations. Systematic reanalyses of archived samples have established the reproducibility of the optical absorption measurements across decades and rebuilt instrument systems, with a consistent calibration that is traceable back to 2003. IMPROVE samples for nondestructive sequential analyses by photometry, gravimetry, and X-ray fluorescence are all collected on ring-mounted membranes of expanded PTFE (polytetrafluoroethylene). Although chemically inert, low in blank mass, and optically thin, these collection media yield visibly nonuniform deposits that do not admit direct interpretation according to a naïve "Beer-Lambert" formulation of optical absorption. Most IMPROVE PTFE deposits exhibit fine-scale "pixelation," a pattern shaped by the perforated metal screen that supports the membrane during sample collection. This paper extends the traditional Beer-Lambert interpretive model to accommodate the patterned deposits generated on PTFE filters by IMPROVE and similar sampling protocols.Implications: The ambient absorption coefficients historically reported from IMPROVE filter samples are based on the conventional Beer-Lambert interpretation of measured transmittance by filter deposits assumed to be uniform. The neglect of observed patterning in actual deposits yields predictable underestimates for the summed absorption cross-sections of the collected particles. This pixelation bias can be substantial for individual samples that are taken from elevated concentrations of absorbing aerosols, near wildfires or urban centers. Over the regional-haze-tracking scale of the predominantly rural IMPROVE network, the aggregate bias relevant to radiative forcing is more moderate, estimated at roughly 10%.

To achieve sustainable development in the electric vehicle (EV) industry, this study assesses the environmental impacts of retired electric vehicle batteries (EVBs) throughout the life cycle. The life cycle assessment (LCA) with the ReCiPe method is implemented with environmental impacts: CO_2eq emissions, human toxicity, terrestrial acidification, particulate matter (PM) formation, metal depletion, and fossil depletion. Four EOL management scenarios, namely the landfilling, remanufacturing, repurposing, and recycling processes, are examined with the background data obtained from the Ecoinvent database v3.6 and data collected from secondary sources. The study results reveal that the landfilling scenario is highly harmful to humans and due to its highest environmental impacts, specifically CO2 emission (2,236 kg CO_2eq) from the material extraction process. In contrast, the recycling scenario is the most environmentally friendly scenario, as it reduces the human toxicity (45,934 kg 1,4-DB_eq), terrestrial acidification (425 kg SO_2eq), and metal depletion (20,129 kg Fe_eq), achieving the lowest final impact score of -277. The study further examines the recycling scenario with different energy mixes, i.e. natural gas, coal, and renewable energy. The results suggest that the complete use of renewable energy could improve the final impact value to -281.1. The results also recommend the remanufacturing scenario as it reduces CO_2eq emission by 1,193 kg CO_2eq. The government may utilize the study results to enhance the circular economy of retired EVBs through various strategies to compete in the global market. A comprehensive evaluation of EOL management practices of retired EVBs offers valuable insights for policymakers and stakeholders to minimize the ecological footprint of the EV industry and support Thailand's sustainability goals. A future study may be performed to compare the EOL management scenarios with actual practices and suggest suitable improvements. The policy-based simulations could be implemented to examine long-term impacts of EOL management practices in Thailand.Implications: This study examines the end-of-life management of electric vehicle batteries (EVBs) through remanufacturing, repurposing, and recycling scenarios. The results show that the recycling scenario is the most effective EOL strategy for retired EVBs as it generates the lowest human toxicity, terrestrial acidification, and metal depletion. Alternatively, the remanufacturing scenario is the most suitable scenario when CO2eq emission is a major concern. The results also recommend at least half of renewable energy to be used in electricity production to improve the final impact of this study.

This study investigated the feasibility of using/reusing commercial activated carbon (CAC) for the capture of high molecular weight and high-boiling point volatile organic compounds (HBPVOCs). The CAC was first characterized using proximate analysis, heat value analysis, iodine value analysis, element analysis, inductively coupled plasma spectrometry, and specific surface area analysis. We then assessed the adsorption/desorption performance of a CAC-based PSA system for the removal of Butyl Cellosolve (BCS), a HBPVOC commonly used in paints, coatings, cleaners, and industrial processes. This involved deriving the BCS adsorption capacity of CAC as a function of adsorbent quantity (2.5, 5, and 10 g), flow rate (4, 6, and 8 L/min), and pressure (1.3, 2.3, and 3.4 kg/cm²). The BCS adsorption capacity of the CAC varied with pressure as follows: 1.3 kg/cm² (652.85 mg/g), 2.3 kg/cm² (817.20 mg/g) and 3.4 kg/cm² (1324.05 mg/g). The adsorption mode most closely resembled pseudo-first-order kinetics (i.e. single-layer physical adsorption). Desorption was performed using an adjustable tubular high-temperature furnace under a nitrogen atmosphere (0.93 kg/cm²). Following desorption with a set desorption duration of 1 hr, the BET values varied with temperature as follows: 350°C (75.58% of the original value) and 450°C (86.04% of the original). Desorbed CAC (DCAC) was also examined to detect changes in pore structure due to the effects of recycling. We obtained breakthrough curves and a dsorption capacity curves of CAC as functions of flow rate and pressure. We also investigated adsorption performance under pressure swing conditions from the perspective of reaction kinetics and density functional theory. Our results demonstrate the efficacy of CAC in the adsorption of BCS as well as the recyclability of this material.Implications: This study demonstrates the potential for reusing commercial activated carbon (CAC) to capture high molecular weight and high-boiling point volatile organic compounds (HBPVOCs). Through comprehensive characterization and performance evaluation, we found that CAC effectively adsorbs Butyl Cellosolve (BCS), a common industrial solvent, with adsorption capacity increasing with pressure. The adsorption process follows pseudo-first-order kinetics, indicating single-layer physical adsorption. Additionally, the study highlights the recyclability of CAC, as desorption and subsequent analysis revealed minimal changes in pore structure, maintaining a significant portion of its original BET value. These findings suggest that CAC is not only effective for BCS adsorption but also sustainable for repeated use, offering an efficient and eco-friendly solution for managing industrial HBPVOCs.

Bioenergy or green fuel has been considered the fuel of the future for being a type of renewable energy that contributes to the preservation of the environment as it helps to reduce greenhouse gas emissions. In this way, biogas offers a potential alternative to fossil fuels from anaerobic digestion (AD) bioprocess, which allows the action of several microorganisms in the transformation of substrates into biogas and secondary bioproducts. Over the years, researchers have discussed that low yields in AD are associated with different factors such as type of wastewater, reactor configuration, substrate concentration, temperature, organic loading rates, and biomass concentration inside of the reactor. In this way, to better conduct the AD, studies point to the reactor configuration as one of the factors in the determination of high biogas production for a long period. Understanding and knowing the type of reactor and how the parameters such as biomass accumulation and immobilization, pH, or temperature occur in the system would provide information and can help to improve the bioenergy production in different systems. Moreover, research opportunities about different technologies are essential for the anaerobic digestion of many substrates and the stability of interest production. Thus, this type of scientific study gives a broad overview of the principal systems used in the AD process and information about the circular economy in the production of biogas in the world. Important considerations are highlighted.Implications: The review paper provides information about the scenario of biogas in the world state-of-art and the biogas production from AD. Afterward, an extensive analysis of different and principal types of reactors applied to the AD process, aimed at presenting an overview of the advantages and disadvantages of each configuration intending to gain new insights to improve traditional reactors or propose novel ones. This article enables us to have a perspective about the different technologies available and about new alternatives from an operational point of view for bioenergy from AD, not only in bench studies or pilot scale studies but also at an industrial level. Thus, this type of scientific study gives a broad overview of the principal systems used in the AD process and information about the circular economy in the production of biogas in the world.

Agricultural plastic greenhouses (APGs) are crucial for sustainable agricultural planting, and accurate spatial distribution information acquisition is crucial. Deep learning network models can extract target features from remote sensing images more effectively than traditional interpretation methods, which face challenges like high workloads and poor repeatability. In this study, we aim to enhance the inventorying of Agricultural Plastic Greenhouses (APGs) by improving the extraction accuracy of their locations and numbers through remote sensing techniques. Utilizing GF-7 satellite imagery, we propose an enhanced U-Net convolutional neural network (CNN) model that incorporates edge information expansion and a joint loss function to optimize performance. The primary objective is to provide a rapid and accurate method for mapping APGs, which is crucial for effective agricultural management and environmental monitoring. The U-Net network's accuracy was enhanced by 1.1% after expanding 3 × 3 sample edge information, and further by 1.9% by combining edge extension and loss function constraints. Our results demonstrate that the modified U-Net model significantly improves extraction accuracy compared to traditional methods, thereby facilitating better inventory management and planning for agricultural cash crops. This advancement not only supports farmers in optimizing resources but also contributes to sustainable agricultural practices by enabling precise monitoring of APG distribution.Implications: Compared to traditional interpretation methods, which suffer problems such as heavy workloads, small adaptation ranges and poor repeatability, deep learning network models can better extract target features from remote sensing images. In this study, we used GF-7 image data to improve the traditional U-Net convolutional neural network (CNN) model. The Canny operator and Gaussian kernel (GK) function were used for sample edge expansion, and the binary cross-entropy and GK functions were used to jointly constrain the loss. Finally, APGs were accurately extracted and compared to those obtained with the original model. The results indicated that the APG extraction accuracy of the U-Net network was improved through the expansion of sample edge information and adoption of joint loss function constraints.

We estimate methane emissions for urban waste treatment facilities from mobile in situ atmospheric concentration measurements using an inverse Gaussian plume methodology at facilities in Southern Ontario, Canada. We use these emission rates to assess and improve the existing high-resolution methane inventories for waste sources throughout Southwestern Ontario. Our measurements encompass tens of thousands of kilometres worth of mobile survey data collected over 7 years, including more than 650 downwind transects where we surveyed 14 active landfills, 11 closed landfills, 2 organic waste processing facilities, 3 open-air windrow compost facilities, and 11 water resource recovery facilities. These sources account for 77% of the active landfills within Southern Ontario, which is estimated in inventories to be the largest source of methane emissions in the region. Within the Greater Toronto Area (GTA) megacity, the measured facilities represent about 52% of the total inventoried non-wetland methane emissions. We find that emissions from closed landfills are lower than inventory estimates, with significant implications for the methane budget in the GTA. We update the Facility Level and Area Methane Emissions for the GTA inventory with our measured emissions rates, which results in a 54% decline in the solid waste emissions, effecting a 35% lower estimate for the total anthropogenic methane emissions in the region. We attribute the bulk of this difference to a single facility: the Keele Valley landfill. Our atmospheric measurements serve as a metric for evaluating the discrepancies between four facility level and two high resolution gridded methane emission inventories. We find that the facility level first-order decay model maintained by Environment and Climate Change Canada (ECCC) to be the most consistent with our measured emission rates at landfills, and the self-reported emissions to the Greenhouse Gas Reporting Program of ECCC to be the least consistent with our measurements.Implications: We present estimates of atmospheric measurement derived methane emissions for multiple waste processing facilities in Canada. We investigate six emission inventories and models. Based on our atmospheric observations of landfills, we show that the self-reported methane emissions are not well correlated with our measured emissions, and that the first order decay models used in official emissions reporting are much better correlated. One of the most critical findings in this work is that methane emissions from the Keele Valley Landfill, assumed in some inventories to be the second largest anthropogenic source of methane in the country, are significantly less than predicted.

Urbanization and infrastructure projects generate huge amount of construction and demolition waste (CDW), posing significant challenges for the environment and human health. In order to reduce the environment and safety risks caused by the CDW landfills, this study was amid to utilize plant roots to develop a root-CDW-soil system for strengthening the CDW and enhancing the slope stability of CDW landfills. A series of experimental analyses were conducted, focusing on shear tests of root-soil composites under various moisture conditions and root content ratios. The results indicate that the inclusion of ryegrass roots plays a critical role in significantly enhancing the shear strength of the soil, and the soil samples reinforced with 0.6 g of ryegrass roots exhibited a shear strength increase of up to 35% compared to the unreinforced samples. The slope stability treated by the plant roots was evaluated by finite element simulations under different rainfall conditions. The factor of safety (FoS) for reinforced slopes increased from 1.18 to 1.59 after five days of heavy rainfall (480 mm/d), highlighting the significant improvement in stability provided by the root systems. These findings suggest that the root-soil system offers a sustainable solution for slope management, reducing risks associated with construction waste and extreme weather conditions.Implications: Urbanization and infrastructure projects generate significant waste, posing environmental and safety challenges. This study investigates the enhancement of slope stability through the integration of ryegrass root systems. The findings indicate that ryegrass roots substantially improve soil shear strength and overall slope stability. Furthermore, the study demonstrates that these root systems enhance resilience to heavy rainfall, thereby mitigating the risk of slope failure. These results suggest that plant root systems offer a sustainable solution for slope management, effectively addressing environmental concerns related to construction waste and extreme weather conditions. This research provides a comprehensive understanding of the physical and mechanical properties of root-soil composites, thereby promoting their practical application in slope stabilization.

The R-LINE model, which was released in 2013 as a stand-alone model for roadway-type applications and was based on a set of newly developed dispersion curves, exhibited favorable model performance in a limited set of evaluations. In 2019, the R-LINE model was incorporated as the RLINE source type in EPA's preferred near-field dispersion model AERMOD. Since its inclusion in AERMOD, the RLINE source type has been tested and compared to other AERMOD source types using multiple data sets and transportation studies. The outcome of these tests is a need to revisit the dispersion parameters used in the original RLINE dispersion curves to address performance issues suggested by comparisons to AREA and VOLUME source types in AERMOD. The work presented here includes corrections to the RLINE vertical wind profiling, harmonization of several aspects of the RLINE formulation with AERMOD's AREA and VOLUME source types (i.e. the addition of terrain and meander weighting), and updates to the RLINE dispersion parameterization based on a computational optimization routine. The updated RLINE source type is compared with AREA and VOLUME estimates for two hot-spot transportation studies. RLINE modeled estimates are also reevaluated with two of the previous evaluation studies and two additional tracer studies. The updated RLINE formulation leads to improved performance in most cases and closer comparison with the AREA and VOLUME sources.Implications: The RLINE source type was recently added by the EPA to the AERMOD model as a "preferred" model option. Thus, the RLINE source type is now available to the air quality modeling community as a modeling option without any approval required. This paper explains recent changes to the model formulation and provides both an updated and expanded model evaluation. For the updated evaluation, we compare the three AERMOD source types (RLINE, AREA, and VOLUME) for two tracer databases used when the RLINE source was initially created (Caltrans 99 and Idaho Falls). We also add model evaluations for two "new" databases (GM Sulfate and Berkeley Freeway Experiment) to expand the assessments of model performance. Additionally, two model intercomparisons are examined, comparing design concentrations for two real-world highway hot-spot projects for RLINE against the AREA and VOLUME sources, which show much better agreement between the three source types with the updated RLINE model. The work is essential for dispersion model practitioners to understand the specifics of RLINE's model formulation as well as its performance against the other two AERMOD source types typically used for modeling roadway emissions.

Implications: In this review paper, we provide a comprehensive overview of approaches for collecting time-activity pattern (TAP) data from individuals, a crucial component in understanding human behavior and its implications across various fields such as urban planning, environmental science, and, particularly, public health in relation to air pollution exposures.Furthermore, our paper introduces and critically evaluates several emerging methods for TAP data collection. These novel approaches, including but not limited to Google Location History, iPhone Significant Locations, and crowdsourced smartphone location data, offer unprecedented granularity in tracking human activities. By showcasing these methodologies and their often not well-recognized weaknesses, we highlight both the potential and limitations of these tools to advance our understanding of human behavior patterns, especially in terms of how individuals interact with their environments. This discussion not only showcases the originality of our work but also sets the stage for future research directions that can benefit from these innovative data collection strategies.