Periodical and regional change of particulate matter and CO2 concentration in Misurata

Comprehensive risk assessment for identifying suitable residential zones in Manavgat, Mediterranean Region

The absence of comprehensive risk analysis in residential development within certain regions often leads to substantial human and material losses during natural disasters. The Mediterranean region, particularly susceptible to the impacts of climate change, is projected to witness an upsurge in the frequency of natural calamities like floods, landslides, and forest fires. Consequently, meticulous risk assessment during the selection of residential areas becomes paramount in this context. This study is dedicated to the evaluation of suitable residential zones in Manavgat, a pivotal location in the Mediterranean region with a progressively growing population. The findings indicate that approximately 4.26 % of the research area is deemed appropriate for residential establishment. The identification of these locations is crucial for ensuring human and material safety, as well as enhancing overall biocomfort. Moreover, this study provides a foundation for long-term planning initiatives within the region and makes a significant contribution to the international evaluation literature by demonstrating the application of integrated risk assessment methodologies in urban planning.

Assessment of metals (Ni, Ba) deposition in plant types and their organs at Mersin City, Türkiye

The increase in heavy metal concentrations in the air, especially after the Industrial Revolution, is notable for the scientific world because of the adverse effects that threaten environmental and human health. Among the trace elements, nickel (Ni) is carcinogenic, and all barium (Ba) compounds are toxic. Trace elements are critical for human and environmental health. Their threat further increases, especially in the urban areas and surroundings with a high population. In urban areas, the trace element contamination in the airborne can be reduced using plants. However, which plant and plant organs absorb trace elements could not be determined. In the present study, Ni and Ba concentrations in the branch, wood, and leaf samples of 14 species collected from the city center of Mersin province were determined. As a result, broad-leaved species' Ni and Ba concentrations in their leaf sample were generally higher than other species. Almost all species had the lowest Ni and Ba concentrations in their wood samples. Among these 14 species, it was found that Ni concentration was very high, especially in non-washed leaves of Platanus orientalis, Photinia serrulata, and Citrus reticulate, and Ba concentration was very high in Citrus reticulata, Chamaecyparis lawsoniana, Laurus nobilis, and Acer hyrcanum. Using broad-leaved species in urban areas where pollution is at high levels will significantly contribute to reducing Ni and Ba pollution. It is recommended that these points be considered in future urban landscaping projects.

Anthraquinone as emerging contaminant: technological, toxicological, regulatory and analytical aspects

Anthraquinone (anthracene-9,10-dione) is a multifaceted chemical used in the paper industry, in the production of synthetic dyes, in crop protection against birds and is released from fossil fuels. Additionally, the anthraquinone scaffold, when substituted with sugars and hydroxyl groups is found in plants as metabolites. Because of these multiple applications, it is produced on a large scale worldwide. However, its toxicological aspects have gained interest, due to the low limits in the foods defined by legislation. Worrying levels of anthracene-9,10-dione have been detected in wastewater, atmospheric air, soil, food packaging and more recently, in actual foodstuffs. Recent investigations aiming to identify the anthracene-9,10-dione contamination sources in teas highlighted the packaging, leaves processing, anthracene metabolism, reactions between tea constituents and deposition from the environment. In this context, this review seeks to highlight the uses, sources, biological effects, analytical and regulatory aspects of anthracene-9,10-dione.

Graphical Abstract

AQI prediction using layer recurrent neural network model: a new approach

The air quality index (AQI) prediction is important to evaluate the effects of air pollutants on human health. The airborne pollutants have been a major threat in Delhi both in the past and coming years. The air quality index is a figure, based on the cumulative effect of major air pollutant concentrations, used by Government agencies, for air quality assessment. Thus, the main aim of the present study is to predict the daily AQI one year in advance through three different neural network models (FF-NN, CF-NN and LR-NN) for the year 2020 and compare them. The models were trained using AQI values of previous year (2019). In addition to main air pollutants like PM10/PM2.5, O3, SO2, NOx, CO and NH3, the non-criteria pollutants and meteorological data were also included as input parameter in this study. The model performances were assessed using statistical analysis. The key air pollutants contributing to high level of daily AQI were found to be PM2.5/PM10, CO and NO2. The root mean square error (RMSE) values of 31.86 and 28.03 were obtained for the FF-NN and CF-NN models respectively whereas the LR-NN model has the minimum RMSE value of 26.79. LR-NN algorithm predicted the AQI values very closely to the actual values in almost all the seasons of the year. The LR-NN performance was also found to be the best in post-monsoon season i.e., October and November (maximum R2 = 0.94) with respect to other seasons. The study would aid air pollution control authorities to predict AQI more precisely and adopt suitable pollution control measures. Further research studies are recommended to compare the performance of LR-NN model with statistical, numerical and computational models for accurate air quality assessment.

The application of machine learning to air pollution research: A bibliometric analysis

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.

Influence of wind direction on the ventilation and pollutant dispersion in different 3D street canyon configurations: numerical simulation and wind-tunnel experiment

Configuration of street canyon and the wind environment have a great influence on the self-ventilation capacity of the canyon, but the couple-effect of these two factors could not be considered in the previous study. The purpose of this study is to clarify the couple effect of street canyon configuration and wind environment on the ventilation and pollutant dispersion inside the street canyon. For this purpose, five wind directions of α = 90°, 60°, 45°, 30°, and 0° (α is the angle between the approaching wind and street canyon) and three canyon configurations (flat, step-up, and step-down canyons) were considered with numerical simulation and wind-tunnel experiment. Meanwhile, ACH (air exchange rate) and NEV (net escape velocity) were used to evaluate the ventilation capacity of the canyon. The results reveal that the wind direction has a vital influence on the ventilation in the different canyon configurations. Under the parallel wind direction (α = 0°), the airflow and ventilation capacity inside the three canyons are similar. Relative difference of ACH, named as RDA ((ACH_asymmetric-ACH_symmetric)/ACH_symmetric [Formula: see text] 100%), is 1.82%. However, under the oblique (α = 30°, 45°, and 60°) and perpendicular wind direction (α = 90°), the airflow of the step-down canyon is very different from the step-up and flat canyons. In step-down canyons, reverse flow occurs under the oblique and perpendicular wind direction, and the strength of the reverse flow increases as α increases. Due to this reverse flow, the ventilation capacity of the step-down canyon is lower than that of the step-up and flat canyons. As for the ventilation capacity in the pedestrian respiration domain, the ventilation capacity of the leeward pedestrian domain (leeward NEV) is higher in the step-down canyon than in the step-up canyon and the flat canyon (when α = 90°, leeward NEV of step-down canyon is 2.47 times the flat canyon). Conversely, the ventilation capacity of the windward pedestrian domain is lower in step-down canyons than in step-up or flat canyon (when α = 90°, windward NEV of step-down canyon is 0.1 times that of step-up canyon). The aforementioned findings are helpful to understand the effects of canyon configurations together with wind directions on the airflow as well as pollutant concentration inside the canyon. Although further researches are still required to provide practical guidelines, this study present effective methodologies to quantify the influences of street configurations and wind directions on street canyon ventilation for urban design purpose.

Determining the suitable settlement areas in Alanya with GIS-based site selection analyses

Urbanization, which is defined as an irreversible global-scale problem nowadays, necessitates the foundation of new settlement areas. In general, no sufficient scientific assessment and analysis were performed during these processes, and thus, various natural disasters cost the loss of many lives and properties every year. Nevertheless, considering the areas that are risky in terms of natural disasters during the selection of settlement areas might prevent a large-scale loss of lives and properties because of natural disasters. Within the scope of this study, it was aimed to determine suitable settlement areas in the Alanya district, which is one of the significant points of interest for tourists in our country and has a large population and new settlement areas because of this increasing population. Within this scope, besides the risks of flood and landslide that are the most important natural disasters in the region, and a forest fire that is among the most significant risks for the region, also the biocomfort zones were included in the assessments. As a result of the study, it was determined that the most important natural disaster risk was flooding in a large portion of the region and that only 6.72% of the study area was suitable for settlement in terms of all the criteria examined in the present study.

Variation of Al concentrations depending on the growing environment in some indoor plants that used in architectural designs

Today, most of people's time is spent indoors. Air pollution indoors is much higher than outdoors. Therefore, the effect of indoor air on human health is much more than the outside air. One of the most effective methods of reducing indoor air pollution is the use of plants. However, in order for the plants to be used effectively for this purpose, it is first necessary to determine which plants are the most effective in reducing which pollutants. Within the scope of this study, the Al accumulation potential of seven ornamental plants, which are frequently used in interior architectural designs, was evaluated. Within the scope of the study, the variation of Al concentrations on the basis of species and environment was determined in plants grown in control, smoking environment, and traffic environment. As a result of the study, it was determined that Al concentrations changed statistically significantly both on the basis of environment and species, and the species with the highest Al accumulation potential was difenbahya.

Energy transition in the enhancement and break of turbulence barrier during heavy haze pollution

Variation of the turbulence barrier effect caused by the turbulence intermittency have a strong impact on the vertical distribution and variation of pollutants, which limits the accuracy of pollution process simulation. Turbulence observation data from the five layers of the 255 m meteorological tower in Tianjin during two severe haze pollution periods were used to discuss energy changes during the enhancement and break of the turbulence barrier. Results showed that a sharp decrease in turbulence kinetic energy contributed to barrier enhancement and the kinetic energy transfer from sub-mesoscale motion to turbulence triggered the barrier break. The barrier break point tends to occur after Δ KE < 0 (the kinetic energy difference between turbulence and sub-mesoscale motion), subsequently followed by a significant increase in Δ KE. Due to the significant reduction in wind speed during severe haze pollution, type-B intermittency events occurred more frequently and existed at five heights. Type-A intermittency events were more likely to occur at the heights of 40 and 80 m, and type-C intermittency events were more likely to occur at heights above 80 m. Wind speed thresholds at different heights (2.5 m s^-1 for 40 m, 4 m s^-1 for 80 m, 4 m s^-1 for 120 m, 4 m s^-1 for 160 m, 4.5 m s^-1 for 200 m) can be used to determine whether turbulent barrier effects occurred. This study provides an important research basis for solving the theoretical problem of the stable boundary layer that currently limits the accurate prediction of severe haze pollution processes.

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach

The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018-23 March 2020), complete lockdown (24 March 2020-31 May 2020), and partial lockdown (1 June 2020-30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM₁₀ and PM_2.5). However, the effect of the lockdown is most prominent on PM_2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM_2.5 and PM₁₀ concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM_2.5 and PM₁₀ during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May-15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96-120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.

Citizens' perception towards landfill exposure and its associated health effects: a PLS-SEM based modeling approach

Around 90% of municipal solid waste in India is treated improperly at open dumps and landfills, posing a severe threat to public health. Landfills are an annoyance whose presence causes uncertainty, stress, and dissatisfaction in neighboring residential areas. This research investigates the perceived impact of exposure to landfills on health in terms of environmental quality, general living status, and defensiveness. To meet the current study's objective, "Case of exposed population" to landfill, i.e., 384 participants dwelling near 1 km of the dump site in Siliguri municipality, West Bengal, India, were considered using a purposive sample approach. The present study adopted Partial Least Square-Structural Equation Modeling (PLS-SEM) approach to prove the hypothesis related to the residential perception of landfills and their impact on health. The findings reveal that all three constructs, namely environmental quality (β = .997, t = 19.607, and ρ < 0.001), general life status (β = .116, t = 2.475 and ρ < 0.05), and people's defensive attitude (β = .150, t = 2.526 and ρ < 0.001), significantly affect the health condition of those exposed to a landfill site. The results suggest that by understanding the impact of landfills on resident health, policymakers and bureaucrats can promote reliable and effective measures linked to sustainable solid waste disposal facilities. The administration must create a policy to protect citizens who live near landfills by improving the ambient environment, establishing health management facilities, and raising awareness through public participation.

Assessment of the exposure to PM2.5 in different Lebanese microenvironments at different temporal scales

The weak potential of using the sole outdoor concentrations to represent personal exposure to PM_2.5 is confirmed by the literature; therefore, it is important to account for a person's movements over time when estimating the short-term personal air pollution exposure within different microenvironments (MEs). This study is an example of applying an assessment method of the exposure to PM_2.5 in different microenvironments at different temporal scales. A low-cost particle counter (the Dylos 1700) was used; its performance was validated in comparison with equivalent instruments such the SidePak AM520 Personal Aerosol Monitor (R² = 0.89). This validation also provided a function to convert measured particle number concentrations (PNCs) into calculated particle mass concentrations. The 150 profiles that was collected on a minute-by-minute basis regarding PM_2.5 concentration from December 2018 to May 2021 highlight the influence of individual activities and contextual factors on the air quality, so that Lebanon's annual PM_2.5 mean (24.2 µg⁄m³) is 142% higher than the World Health Organization (WHO) annual mean guideline (10 µg⁄m³). Winter is the most polluted period due to the increased application of space heating devices. Additionally, the occurrence of dusty winds during the spring period leads to the elevated levels of dispersed PM_2.5. Simultaneously, the rural zones are more polluted than urban ones due to the usage of more traditional heating equipment, in addition to the usage of chemical products like pesticides and fertilizers in agricultural activities in such areas. Furthermore, the (outdoor-indoor-transport) MEs indicate that the transport and indoor MEs have similar levels of suspended fine particulates, while outdoor MEs are less polluted. Studies based on the personal exposure to PM_2.5 were generally applied on specific and limited places such as schools, workplaces, or residences. The study aims to shed light on the modern method in an attempt to estimate the personal exposure to PM_2.5 and to inspire similar studies to achieve the maximum efficiency.

An analysis of particulate pollution using urban aerosol pollution island intensity over Delhi, India

The accent of the present study is determination of Urban Aerosol Pollution Island (UAPI) intensity and spatial variability in particulate matter concentration (PM10 and PM2.5) over Delhi. For analysis, the hourly concentration dataset of PM2.5 and PM10 from January 2019 to December 2020 was obtained from ten air quality monitoring stations of Delhi. Additionally, UAPI Index has been calculated to assess the intensity of particulate pollution. The daily, monthly, and annual variations in the trends of PM10, PM2.5, and UAPI index along with related meteorological parameters have been analyzed. Particulate pollution peaked majorly during two seasons, i.e., summer and winter. The highest concentration of PM10 was observed to be 426.77 µg/m3 while that of PM2.5 was observed to be 301.91 µg/m3 in January 2019 for traffic-affected regions. During winters, higher PM2.5 concentration was observed which can be ascribed to increased local emissions and enhanced secondary particle formations. While the increase in PM10 concentrations led to an increment in pollution episodes during summers over most of the sites in Delhi. The UAPI index was found to be declining in 2020 over traffic affected regions (77.92 and 27.22 for 2019 and 2020, respectively) as well as in the background regions (64.91 and 19.80 for 2019 and 2020, respectively) of Delhi. Low traffic intensity and reduced pollutant emission could have been responsible for the reduction of UAPI intensity in the year 2020. The result indicates that lockdown implemented to control the COVID-19 outbreak led to an unexpected decrease in the PM10 pollution over Delhi.

Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10

Kolkata has a reputation for being one of the world's most polluted cities, particularly in the post-monsoon months of October, November, and December. Diwali, a Hindu festival, coincides with these months where a large number of firecrackers are set off followed by high emissions of air pollutants. As a result, the air quality index (AQI) deteriorates to "very poor" (301 ≤ AQI ≤ 400) and "poor" (201 ≤ AQI ≤ 300) categories. This situation stays for several days to a month. The present study aims to identify the thresholds for PM_2.5 and PM₁₀ that cause the AQI of Kolkata to deteriorate to "very poor" and "poor." For this purpose, we have used a rough set theory-based condition-decision support system to predict the aforementioned categories of AQI. We have developed a Z-number-based novel quantification measure of semantic information of AQI to assess the reliability of the outcomes, as generated from the condition-decision-based decision rules, during post-monsoon season. The result reveals the best possible forecast of AQI with linguistic summarization of the reliability or confidence for different threshold ranges of PM₁₀ and PM_2.5. Inverse-decision rules based on rough set theory are utilized to justify and validate the forecasts. The explainability of the condition-decision support system is demonstrated/visualized using a flow graph that maps rough-rule-based different decision paths between input and output with strength, certainty, and coverage. The investigation resulted in an advanced intelligent environmental decision support system (IEDSS) for air-quality prediction.