Journal of the Air & Waste Management Association最新文献

Impressions of a place are partly formed by smell. The urban waterfronts often leave a rather poor impression due to odor pollution, resulting in recurring complaints. The nature of such complaints can be subjective and vague, so there is a growing interest in quantitative measurements of emissions to explore the causes of malodorous influence. In the present work, an air quality monitor with an H₂S sensor was employed to continuously measure emissions of malodors at 1-min resolution. H₂S is often considered to be the predominant odorous substance from sludge and water bodies as it is readily perceptible. The integrated means of concentration from in situ measurements were combined with the AERMOD dispersion model to reveal the spatial distribution of odor concentrations and estimate the extent of odor-prone areas at a daily time step. Year-long observations showed that the diurnal profile exhibits a positively skewed distribution. Meteorology plays a vital role in odor dispersion; the degree of dispersion was explored on a case-by-case basis. There is a greater likelihood of capturing the concentration peaks at night (21:00 to 6:00) as the air is more stable then with less tendency for vertical mixing but favors a horizontal spread. This study indicates that malodors are changeable in time and space and establishes a new approach to using H₂S sensor data and resolves a long-standing question about odor in Hong Kong.Implications: this study establishes a new approach combining dispersion model with novel H₂S sensor data to understand the characteristics and pattern of odor emanated from the urban waterfront in Hong Kong. The sensor has dynamic concentration range to detect the episodic level of H₂S and low level at background conditions. It provides more complete information in relation to odor annoyance, as well as quantitative information useful for odor regulation.

We investigated the impact of wildfires on maximum daily 8-hr average ozone concentrations (MDA8 O₃) at four sites in Salt Lake City (SLC), Utah for May to September for 2006-2022. Smoke days, which were identified by a combination of overhead satellite smoke detection and surface PM_2.5 data and accounted for approximately 9% of the total number of days, exhibited O₃ levels 6.8 to 8.9 ppb higher than no-smoke days and were predominantly characterized by high daily maximum temperatures and low relative humidity. A Generalized Additive Model (GAM) was developed to quantify the impact of wildfire contributions to O₃. The GAM, which provides smooth functions that make the interpretation of relationships more intuitive, employed 17 predictors and demonstrated reliable performance in various evaluation metrics. The mean of the residuals for all sites was approximately zero for the training and cross-validation data and 5.1 ppb for smoke days. We developed three approaches to estimate the contribution of smoke to O₃ from the model residuals. These generate a minimum and maximum contribution for each smoke day. The average of the minimum and maximum wildfire contributions to O₃ for the SLC sites was 5.1 and 8.5 ppb, respectively. Between 2006 and 2022, an increasing trend in the wildfire contributions to O₃ was observed in SLC. Moreover, trends of the fourth-highest MDA8 O₃ before and after removing the wildfire contributions to O₃ at the SLC Hawthorne site in 2006-2022 were quite different. Whereas the unadjusted data do not meet the current O₃ standard, after removing the contributions from wildfires the SLC region is close to achieving levels that are consistent with meeting the O₃ standard. We also found that the wildfire contribution during smoke days was particularly high under conditions of high temperature, high PM_2.5 concentration, and low cloud fraction.Implications: In this study, we quantified the impact of wildfires on maximum daily 8-hr average ozone concentrations (MDA8 O₃) in Salt Lake City, Utah, using a Generalized Additive Model (GAM). The GAM results demonstrate the importance of wildfires as contributors to O₃ air pollution. Our results suggest that states could use the GAM approach to assist in quantifying the wildfire contribution to MDA8 O₃ under the U.S. EPA exceptional events rule. These findings also highlight the need for strategies to manage wildfires and their subsequent impacts on air quality in an era of climate warming.

Indoor air pollution remains a major concern, with formaldehyde (HCHO) a primary contributor due to its long emission period and associated health risks, including skin allergies, coughing, and bronchitis. This study evaluated the adsorption performance and economic efficiency of various adsorbents (biochar, activated carbon, zeolites A, X, and Y) selected for HCHO removal. The impact of thermal treatment on adsorbent regeneration was also assessed. The experimental apparatus featured an adsorption column and HCHO concentration meter with an electrochemical sensor designed for adsorption analysis. Zeolite X exhibited the highest adsorption performance, followed by zeolite A, zeolite Y, activated carbon, and biochar. All adsorbents displayed increased HCHO removal rates with an extended length/diameter (L/D) ratio of the adsorption column. Zeolite A demonstrated the highest economic efficiency, followed by zeolite X, activated carbon, zeolite Y, and biochar. Higher L/D ratios improved economic efficiency and prolonged the replacement cycle (the optimal timing for adsorbent replacement to maintain high adsorption performance). Sensitivity analysis of adsorbent regeneration under varying thermal treatment conditions (150, 120, and 80°C) and durations (60, 45, and 30 min) revealed minimal changes in adsorption efficiency (±3%). The results indicated the potential of adsorbent regeneration under energy-efficient thermal treatment conditions (80°C, 30 min). In conclusion, this study underscores the importance of a comprehensive assessment, considering factors such as adsorption performance, replacement cycle, economic efficiency, and regeneration performance for the selection of optimal adsorbents for HCHO adsorption and removal.Implications: This study underscores the importance of adsorption technology for the removal of formaldehyde and similar volatile organic compounds (VOCs), highlighting the potential of alternative adsorbents, such as environmentally friendly biochar, in addition to traditional strategies, such as activated carbon and zeolites. Our findings demonstrate the feasibility of adsorbent regeneration under energy-efficient thermal treatment conditions. These results hold promise for improving indoor air quality, reducing environmental pollutants, and enhancing responses to air contaminants like fine dust and VOCs.

Since the outbreak of COVID-19 few years ago, the increasing of the number of medical waste has become a huge issue because of their harmful impact to environment. A major concern associated to the limitation of technologies for dealing with medical waste, especially conventional technologies, are overcapacities since pandemic occurs. Moreover, the outbreak of new viruses from post COVID-19 should become a serious attention to be prevented not only environmental issues but also the spreading of viruses to new pandemic near the future. The high possibility of an outbreak of new viruses and mutation near the future should be prevented based on the experience associated with the SARS-CoV-2 virus in the last 3 yr. This review presented information and strategies for handling medical waste during the outbreak of COVID-19 and post-COVID-19, and also information on the current issues related to technologies, such as incineration, pyrolysis/gasification, autoclaves and microwave treatment for the dealing with high numbers of medical waste in COVID-19 to prevent the transmission of SARS-CoV-2 virus, their advantages and disadvantages. Plasma technology can be considered to be implemented as an alternative technology to deal with medical waste since incinerator is usually over capacities during the pandemic situation. Proper treatment of specific medical waste in pandemics, namely face masks, vaccine vials, syringes, and dead bodies, are necessary because those medical wastes are mediums for transmission of the SARS-CoV-2 virus. Furthermore, emission controls from incinerator and plasma are necessary to be implemented to reduce the high concentration of CO₂, NO_x, and VOCs during the treatment. Finally, future strategies of medical waste treatment in the perspective of potential outbreak pandemic from new mutation viruses are discussed in this review paper.Implications: Journal of the air and waste management association may consider our review paper to be published. In this review, we give important information related to the technologies, managements and strategies for handling the medical waste and control the transmission of SARS-CoV-2 virus, starting from proper technology to control the high number of medical waste, their pollutants and many strategies for controlling the spreading of SARS-CoV-2 virus. Moreover, this review also describes some strategies associated with control the transmission not only the SARS-CoV-2 virus but also the outbreak of new viruses near the future.

The work status of ships' engines and boilers has a significant impact on emission estimates, which are closely related to ships' operational phases. To improve the accuracy of emission estimates, this study proposed a machine learning-based classification model for identifying operational phases. We proposed 12 operational phase relevance features by analyzing motion behavior-related and geospatial characteristics-related features from the Automatic Identification System (AIS) data from the two bulk carriers. The random forest (RF) model showed the best performance in identifying one of the bulk carrier's operational phases among the five machine models, with the accuracy, F₁score and Area Under Curve (AUC) of 96.66%, 93.34% and 99.93%, respectively. By adopting the Progressive Ablation Feature Selection (PAFS) method with RF, the number of features was reduced from 12 to 8, and the accuracy (96.38%), F₁score (92.70%), and AUC (98.81%) were almost same with that obtained from all 12 features. Additionally, the effectiveness of the RF model was validated on the other bulk carriers. Compared with the traditional algorithms, the RF model showed better performance in ship operational phase identification and improved the average accuracy of NOx emission estimation for the main engine and auxiliary engine by 57.83% and 93.89%, respectively, under different operational phases. These results provide the basis for port traffic management and ship emission control.Implications: A new ship operational phase identification approach was proposed in this study. If the proposed approach is adopted by International Maritime Organization, it will improve the accuracy of ship emission estimates and bring new insights into global shipping greenhouse gas (GHG) emissions and their impact on global change. The port authorities could benefit from the proposed approach, which can be extended to ship types with similar behavior to bulk carriers, such as containers and general cargoes. This can reveal patterns of ship behavior in specific areas, which helps to identify potential collision risks, channel blockages, and other safety issues and take appropriate management measures to ensure the safe operation of the port. The proposed approach can help shipping companies to accurately estimate the GHG emissions of their fleets and to accurately predict carbon tax costs. Base on that, carbon emissions and carbon tax burden can be reduced by adopting corresponding management control measures.

E-waste is a valuable secondary resource containing numerous toxic substances and high-value components. If improperly handled, it will cause severe environmental pollution. Therefore, efficient recycling of this material can reduce environmental pollution. However, after crushing, fine crushing, and magnetic separation, a substantial quantity of fragmented non-magnetic materials with high value, such as copper andg aluminum, remain. Refrigerators, as typical e-waste, have a similar composition to fragmented non-magnetic materials. Consequently, this paper focuses on the issues of low efficiency, environmental pollution, and resource waste in sorting fragmented non-magnetic materials from waste refrigerators. This paper constructs a data set of fragmented non-magnetic materials of refrigerators, augments the data set, and identifies fragmented non-magnetic materials of refrigerators using a computer vision-based deep learning method. In this study, YOLOv5s is used as the benchmark model. The CBAM module is added to the backbone to enable intelligent identification and sorting of fragmented non-magnetic materials in refrigerators. The final identification efficiency of waste refrigerators meets the requirements of industrial applications, with an accuracy rate of 98.3%, a recall rate of 96.8%, and an average accuracy of 98%. Based on the similarity of the composition of e-waste fragmented materials, this model sorting method can be applied to sorting additional e-waste fragmented materials. Furthermore, it provides the theoretical foundation for promoting e-waste resourcefulness.Implications: This paper proposes a recognition model based on YOLOv5s to solve the problems of low sorting efficiency, environmental pollution, harm to health, and resource waste of non-magnetic crushed material from refrigerators. The recognition model principally addresses the following issues: a deep learning model is developed for recognition and sorting to improve e-waste recognition and sorting efficiency. Concerning the issue of environmental benefits in an ecological environment, a vision-based automatic identification method is proposed to sort harmful waste, such as foam, to preserve the ecological environment. In response to the problem of resource waste, this project improves the purity of precious metals, resulting in a recovery rate of 99.1% for copper and 96.44% for aluminum. In other words, the cost of recovering metals has increased. The identification model of non-magnetic crushed material in refrigerators satisfies production identification and sorting requirements. In addition, the method has application and promotion value, sorting a theoretical foundation and method for identifying and classifying e-waste.

Air pollution can have deleterious impacts on human health and the environment. Historically, air pollution studies have focused more on cities. However, it is also important to consider the impact on large suburban populations living closer to the major cities. In this study, nitrogen oxides (nitrogen dioxide and nitric oxide), sulfur dioxide, ozone, and ammonia concentrations were measured from fifteen sites in the Greater Philadelphia area, Pennsylvania, USA using Ogawa passive samplers from September 2021 to May 2022. The fall season had the highest mean NOx concentrations (11.03 ± 4.51 ppb), and spring had the highest mean O₃ concentration (18.65 ± 6.71 ppb) compared to other seasons. NOx concentrations were higher at suburban (30.43 ± 33.79 ppb) and urban sites (22.49 ± 12.54 ppb) compared to semi-rural sites (11.08 ± 9.20 ppb). SO₂ was not detected in most of the measurements. The positive statistically significant correlation between NO and NH₃ in urban (R² = 0.33, p-value <0.05) and suburban sites (R² = 0.37, p-value <0.05) during winter and spring, respectively, suggests a high attribution of traffic emissions to NH₃ at urban and suburban sites. Influence of traffic emissions on air pollutant values for the study region is also supported by similar NOx concentrations between suburban and urban sites as well as decreasing NO₂/NOx ratios with increased distance from expressways. This study shows that passive sampling can be effectively used for assessing spatial and seasonal variations in air pollutants within an area of diverse land use.Implications: This study presents the findings of temporal and seasonal patterns for nitrogen dioxide, nitric oxide, tropospheric ozone, and ammonia at urban, suburban, and semi-rural areas of the greater Philadelphia region. The main objective of the study is to monitor air pollution in suburban and semi-rural areas which are not monitored for air pollution. We monitored from a total of fifteen sites in three seasons to assess air pollution in suburban and semi-rural areas near the major city in the United States - Philadelphia. The findings are important to learn how air quality is affected in suburban and semi-rural areas near the major city. The study also shows the useful application of inexpensive passive sampling technique for measuring air pollution.

This paper focuses on the impact of solid barriers located upwind of a highway in reducing vehicle related concentrations that occur downwind of the roadway, compared to a highway without barriers. Measurements made in the United States Environmental Protection Agency's meteorological wind tunnel show that the mitigating impact of an upwind barrier is comparable to that of a downwind barrier. Upwind barriers lead to reductions in pollution concentrations by drawing emissions in from the highway toward the barrier. The emissions are then entrained into the flow above the recirculation zone and dispersed vertically as they are advected downwind. This upwind transport of vehicle emissions leads to concentrations at the center of the roadways that are roughly 200-300% higher than those measured on roadways with downwind barriers. This difference between on-road concentrations indicates that although both types of barriers mitigate the impact of vehicle emissions downwind of a roadway, the upwind barrier may create adverse air quality impacts for the people on the road.We have formulated a semiempirical dispersion model that incorporates the physics revealed by the wind tunnel measurements. This model improves upon a model proposed by Ahangar et al. (2017) by adjusting the wind speed to get a more realistic plume dispersion just downwind of the upwind barrier and also by providing vertical profiles of concentrations in addition to ground-level concentrations. The upwind barrier model proposed in this paper and the downwind barrier model described in Francisco et al. (2022) have been incorporated into AERMOD (version 21112) as a nonregulatory option, including the new two-barrier option when modeling both barriers on the same roadway.Implications: Our paper presents an air dispersion model algorithm for modeling the effect of upwind noise barriers on dispersion of traffic-related emissions from roadways, which was incorporated into EPA's AERMOD and then evaluated using observations from a wind tunnel experiment. The results are compared and contrasted with results from both a no-barrier case and downwind barrier cases. This manuscript expands on previously published work analyzing the effect of barrier height and source-to-barrier distance on downwind dispersion (Atmos. Pollut. Res., 13:101385, 2022, https://doi.org/10.1016/j.apr.2022.101385). The current manuscript uses the same wind tunnel setup as reported there, but focuses on a different subset of cases, namely the upwind barrier cases, when developing dispersion model algorithms to simulate the observed effects. We believe the evaluations of the vertical profiles from the wind tunnel study, development, and incorporation of the upwind barrier algorithms into AERMOD, and model evaluation of these new algorithms are significant contributions to understanding the effects of these commonly used roadside barriers.