Pollution Levels Research Articles

Distribution of volatile organic compounds by distance from industrial complexes and potential health impact on the residents.

Volatile organic compounds (VOCs) are the air pollutants emitted from the petrochemical industry known to pose adverse health effects on workers. The database based on the third phase of The Environmental Health Study in the Korean National Industrial Complexes (EHSNIC) in Ulsan conducted from 2018 to 2021 was used. Subjects were divided into the exposed and control group according to the estimated pollution level and distances from the industrial complexes. Ambient benzene, ethylbenzene, and xylene were significantly higher in the exposed group compared to the controls, as well as their metabolites. Risk of chronic disease and atopic dermatitis was higher in the exposed group which was supported by higher serum inflammatory markers and high hazard index of the exposed region. These results can draw attention to people engaged with environmental plans and used as primary data when making policies to reduce pollutant levels around industrial complexes.

A critical review of biochar as an environmental functional material in soil ecosystems for migration and transformation mechanisms and ecological risk assessment.

At present, biochar has a large application potential in soil amelioration, pollution remediation, carbon sequestration and emission reduction, and research on the effect of biochar on soil ecology and environment has made positive progress. However, under natural and anthropogenic perturbations, biochar may undergo a series of environmental behaviors such as migratory transformation, mineralization and decomposition, and synergistic transport, thus posing certain potential risks. This paper outlines the multi-interfacial migration pathway of biochar in "air-soil-plant-animal-water", and analyzes the migration process and mechanism at different interfaces during the preparation, transportation and application of biochar. The two stages of the biochar mineralization process (mineralization of easily degradable aliphatic carbon components in the early stage and mineralization of relatively stable aromatic carbon components in the later stage) were described, the self-influencing factors and external environmental factors of biochar mineralization were analyzed, and the mineral stabilization mechanism and positive/negative excitation effects of biochar into the soil were elucidated. The proximity between field natural and artificially simulated aging of biochar were analyzed, and the change of its properties showed a trend of biological aging>chemical aging>physical aging>natural aging, and in order to improve the simulation and prediction, the artificially simulated aging party needs to be changed from a qualitative method to a quantitative method. The technical advantages, application scope and potential drawbacks of different biochar modification methods were compared, and biological modification can create new materials with enhanced environmental application. The stability performance of modified biochar was compared, indicating that raw materials, pyrolysis temperature and modification method were the key factors affecting the stability of biochar. The potential risks to the soil environment from different pollutants carried by biochar were summarized, the levels of pollutants released from biochar in the soil environment were highlighted, and a comprehensive selection of ecological risk assessment methods was suggested in terms of evaluation requirements, data acquisition and operation difficulty. Dynamic tracing of migration decomposition behavior, long-term assessment of pollution remediation effects, and directional design of modified composite biochar materials were proposed as scientific issues worthy of focused attention. The results can provide a certain reference basis for the theoretical research and technological development of biochar.

Integrated framework to assess soil potentially toxic element contamination through 3D pollution analysis in a typical mining city.

Soil potentially toxic elements (PTEs) pollution of contaminated sites has become a global environmental issue. However, given that previous studies mostly focused on pollution assessment in surface soils, the current status and environmental risks of potentially toxic elements in deeper soils remain unclear. The present study aims to cognize distribution characteristics and spatial autocorrelation, pollution levels, and risk assessment in a stereoscopic environment for soil PTEs through 3D visualization techniques. Pollution levels were assessed in an integrated manner by combining the geoaccumulation index (Igeo), the integrated influence index of soil quality (IICQs), and potential ecological hazard index. Results showed that soil environment at the site was seriously threatened by PTEs, and Cu and Cd were ubiquitous and the predominant pollutants in the study area. The stratigraphic models and pollution plume simulation revealed that pollutants show a decreasing trend with the deepening of the soil layer. The ranking of contamination soil volume is as follows: Cu > Cd > Zn > As > Pb > Cr > Ni. According to the IICQs evaluation, this region was subject to multiple PTE contamination, with more than 60% of the area becoming seriously and highly polluted. In addition, the ecological hazard model revealed the existence of substantial ecological hazards in the soils of the site. The integrated potential ecological risk index (RI) indicated that 45.7%, 10.13%, and 4.15% of the stereoscopic areas were in considerable, high, and very high risks, respectively. The findings could be used as a theoretical reference for applying multiple methods to integrate evaluation through 3D visualization analysis in the assessment and remediation of PTE-contaminated soils.

Investigation the Level of Groundwater Pollution with Heavy Metals in Mazar-e-Sharif City

Investigation the Level of Groundwater Pollution with Heavy Metals in Mazar-e-Sharif City

Machine learning-based detection of sudden air pollutant level changes: impacts on public health

Machine learning-based detection of sudden air pollutant level changes: impacts on public health

Recent progress in defect-engineered metal oxides for photocatalytic environmental remediation.

Rapid industrial advancement over the last few decades has led to an alarming increase in pollution levels in the ecosystem. Among the primary pollutants, harmful organic dyes and pharmaceutical drugs are directly released by industries into the water bodies which serves as a major cause of environmental deterioration. This warns of a severe need to find some sustainable strategies to overcome these increasing levels of water pollution and eliminate the pollutants before being exposed to the environment. Photocatalysis is a well-established strategy in the field of pollutant degradation and various metal oxides have been proven to exhibit excellent physicochemical properties which makes them a potential candidate for environmental remediation. Further, with the aim of rapid industrialization of photocatalytic pollutant degradation technology, constant efforts have been made to increase the photocatalytic activity of various metal oxides. One such strategy is the introduction of defects into the lattice of the parent catalyst through doping or vacancy which plays a major role in enhancing the catalytic activity and achieving excellent degradation rates. This review provides a comprehensive analysis of defects and their role in altering the photocatalytic activity of the material. Various defect-rich metal oxides like binary oxides, perovskite oxides, and spinel oxides have been summarized for their application in pollutant degradation. Finally, a summary of existing research, followed by the existing challenges along with the potential countermeasures has been provided to pave a path for the future studies and industrialization of this promising field.

Water hyacinths retain river plastics.

Rivers are main conduits for the delivery of plastics to the sea, while also functioning as reservoirs for plastic retention. In tropical regions, rivers are exposed to both high levels of plastic pollution and invasion of water hyacinths. This aquatic plant forms dense patches at the river surface that drift due to winds and currents. Recent work suggests that water hyacinths play a crucial role in influencing plastic transport, by efficiently trapping the majority of surface plastic within their patches. However, a comprehensive understanding of the interaction between water hyacinths and plastics is still lacking. We hypothesize that the properties relevant to plastic transport change due to their trapping in water hyacinth patches. In particular, the length scale, defined as the characteristic size of the transported material, is a key property in understanding how materials move within rivers. Here, we show that water hyacinth patches trap on average 54%-77% of all observed surface plastics at the measurement site (Saigon river, Vietnam). Both temporally and spatially, we found that plastic and water hyacinth presence co-occur. The formation of plastic-plant aggregates carries significant implications for both clean-up and monitoring purposes, as these aggregates can be detected from space and need to be jointly removed. In addition, the length scale of trapped plastics (4.0 m) was found to be forty times larger than that of open water plastics (0.1 m). The implications of this increased length scale for plastic transport dynamics are yet to be fully understood, calling for further investigation into travel distances and trajectories. The effects of plastic trapping likely extend to other key properties of plastic-plant aggregates, such as effective buoyancy and mass. Given the prevalence of plant invasion and plastic pollution in rivers worldwide, this research offers valuable insights into the complex environmental challenges faced by numerous rivers.

Sensitivity Simulations of Wind-driven Water Circulation in İzmit Bay

İzmit Bay, a 50 km long inlet at the eastern end of the Marmara Sea, is crucial for the region’s economy, culture, and marine ecosystem. The bay’s water circulation regulates nutrient distribution, stratification, sedimentation, oxygen levels, heat, and pollution levels. It is also influenced by meteorological events, such as short-term moderate to strong wind conditions. This study investigated the sensitivity of İzmit Bay Water Circulation to wind speed, direction, and duration using the MITgcm model with Orlanski boundary conditions and process-oriented modeling. The simulations showed that under weak forcing conditions, seawater temperature, salinity, and stratification do not significantly vary. However, strong forcing and wind speeds (statistically defined by percentiles of observation data) of 4.9 m/s (75%), 6.7 m/s (90%), and 10.1 m/s (99%) generate significant mesoscale and sub-mesoscale processes, depending on the direction. Westerly component winds cause downwelling at the eastern coastline, while easterly component winds bring sub-surface water to the surface. Strong winds from N, NE, and E sectors lead to the rise in lower-layer waters in the western basin, forcing them to overflow through the Hersek Delta sill into the central basin. Overall, severe wind events greater than 4.9 m/s (75%) significantly affect the bay’s hydrography by transforming the upper layer, with a decrease in temperature up to 5 °C and an increase in salinity up to 10 ppt.

Experimental Investigation of Parameters Influencing the Formation of Dry Bands and Related Electric Field

This paper presents an experimental investigation conducted to determine the influence of parameters such as the ambient temperature, pollution level, and substrate material on the formation of dry bands on polluted layers. To investigate these parameters, we applied a simplified insulator geometry, developed in our previous work, to experimentally control the complex process of dry band formation on a polluted surface. The simple geometry of the experimental model enabled the use of Plexiglas, RTV, and glass as construction substrate materials. RTV and glass were used to simulate a composite and ceramic insulator surface, respectively. Moreover, an electrooptic (EO) probe enabled the measurement of the axial E-field evolution at the surface of the dry band during dry band formation. The results indicated that the substrate material, ambient temperature, and pollution level substantially influence dry band formation. The effects of the first two parameters are directly associated with heat transfer phenomena in the substrate material and at the ambient air/substrate interface. The effect of the third parameter is associated with absorption and evaporation of the pollution layer. In addition, the appearance of the dry band can be clearly identified by a rapid increase in both the pollution layer resistance and the axial E-field in the dry band area. The value of the axial E-field is influenced primarily by the width of the dry band and by the pollution layer resistance, which is directly dependent on the humidification duration. Finally, because most of the results obtained herein were in accordance with those in the literature, we conclude that the proposed experimental model may provide an effective and inexpensive testing method for developing new materials and solutions for improving the dielectric performance of insulators used in polluted environments. Similarly, the simple geometry of the experimental model and the ability to easily control the experimental parameters may enable this tool to validate the results of various numerical models in studies of the thermoelectrical behavior of polluted insulators.

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.

Uncoupled: investigating the lack of correlation between the transcription of putative plastic-degrading genes in the global ocean microbiome and marine plastic pollution

BackgroundPlastic pollution is a severe threat to marine ecosystems. While some microbial enzymes can degrade certain plastics, the ability of the global ocean microbiome to break down diverse environmental plastics remains limited. We employed metatranscriptomic data from an international ocean survey to explore global and regional patterns in microbial plastic degradation potential.ResultsOn a global oceanic scale, we found no significant correlation between levels of plastic pollution and the expression of genes encoding enzymes putatively identified as capable of plastic degradation. Even when looking at different regional scales, ocean depth layers, or plastic types, we found no strong or even moderate correlation between plastic pollution and relative abundances of transcripts for enzymes with presumed plastic biodegradation potential. Our data, however, indicate that microorganisms in the Southern Ocean show a higher potential for plastic degradation, making them more appealing candidates for bioprospecting novel plastic-degrading enzymes.ConclusionOur research contributes to understanding the complex global relationship between plastic pollution and microbial plastic degradation potential. We reveal that the transcription of putative plastic-degrading genes in the global ocean microbiome does not correlate to marine plastic pollution, highlighting the ongoing danger that plastic poses to marine environments threatened by plastic pollution.

Advancing air quality forecasting in Abu Dhabi, UAE using time series models

This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi’s Vision 2030.

Microbial nanomaterials: Role in sustainable agricultural practices

The increased use of chemical fertilizers, pesticides, soil-based heavy metals to improve productivity in agriculture results into higher pollution level in agroecosystem. It has become a major issue on a global scale that seriously jeopardizes people’s health and welfare as well as affects society and overall economy. In view of this, there is an urgent need to detect pollutants in the agroecosystem so that remediation practices can be implemented in time. The evolution of more responsive, affordable and precise technology is needed to create nano-sensors or nanodevices that can be used to monitor agricultural pollution, and the processes involved in pollution control. Major focused ones are those that can detect pollutants at very low concentration levels, as well as point-to-point sensing, in situ, and uninterrupted monitoring devices that offer synchronized data. These sensors are readily available in the market for the detection of organic pollutants, gases, volatile organic molecules, heavy metal ions, hazardous metal ions, etc. but they consume a lot of power and lack sophisticated technologies for improved selectivity and sensitivity. Their precision, sensitivity, response speed and environmental stability under actual operating conditions can all be improved with the aid of nanotechnology. In this study an attempt was made to describe how microbial biosensors can be used to detect toxic materials in agroecosystems. Additionally, an application of nanomaterials as pesticides and antimicrobial agent has also been discussed.

Short-Term Effects of Ambient Air Pollution on Chronic Obstructive Pulmonary Disease Admissions in Jiuquan, China

Recent findings indicate that air pollution contributes to the onset and advancement of chronic obstructive pulmonary disease (COPD). Nevertheless, there is insufficient research indicating that air pollution is linked to COPD in the region of inland northwest China. Daily hospital admission records for COPD, air pollutant levels, and meteorological factor information were collected in Jiuquan for this study between 1 January 2018 and 31 December 2019. We employed a distributed lag non-linear model (DLNM) integrated with the generalized additive model (GAM) to assess the association between air pollution and hospital admissions for COPD with single lag days from lag0 to lag7 and multiday moving average lag days from lag01 to lag07. For example, the pollutant concentration on the current day was lag0, and on the prior 7th day was lag7. The present and previous 7-day moving average pollutant concentration was lag07. Gender, age, and season-specific stratified analyses were also carried out. It is noteworthy that the delayed days exhibited a different pattern, and the magnitude of associations varied. For NO2 and CO, obvious associations with hospitalizations for COPD were found at lag1, lag01–lag07, and lag03–lag07, with the biggest associations at lag05 and lag06 [RR = 1.015 (95%CI: 1.008, 1.023) for NO2, RR = 2.049 (95%CI: 1.416, 2.966) for CO], while only SO2 at lag02 was appreciably linked to hospitalizations for COPD [1.167 (95%CI: 1.009, 1.348)]. In contrast, short-term encounters with PM2.5, PM10, and O3 were found to have no significant effects on COPD morbidity. The lag effects of NO2 and CO were stronger than those of PM2.5 and PM10. Males and those aged 65 years or older were more vulnerable to air pollution. When it came to the seasons, the impacts appeared to be more pronounced in the cold season. In conclusion, short-term encounters with NO2 and CO were significantly correlated with COPD hospitalization in males and the elderly (≥65).

Determination of threshold values and heavy metal pollution assessment of soils in an industrial area in Ghana.

Industrial activities have the potential to pollute soils with a wide variety of heavy metals (HMs). In Ghana, however, assessment of HM pollution of soils in industrial areas remains limited. Accordingly, HM soil pollution in one of the industrial areas in Accra, Ghana was assessed. Soil samples were taken and analysed forHMs, including Fe, Zr, Zn, Ti, Sr, Rb, Mn, Pb, Cu, and Co, using X-Ray Fluorescence (XRF). HM geochemical threshold values (GTVs) were determined to establish soil HM pollution levels and identify areas needing remediation. Furthermore, risk assessments wereconducted to evaluate the potentialecological and human health risks associated with these metals.The mean concentrations of Fe, Zn, Rb, Sr, Zr, Ti, Mn, Co, Cu, and Pb in the soils were: 27133.83, 147.72, 16.30, 95.95, 307.11, 4663.66, 289.85, 418.54, 44.97, and 112.88 mg/kg, respectively. Generally, the concentrations of HMsdecreased with depth, although some lower layers exhibited elevated HM levels. Soil pollution levels were categorized as low for Fe, Rb, Zr, Ti, Mn, Co, and Cu; moderate for Sr and Zn; and considerable for Pb. Notably, the northwestern part of the study area displayed a considerable to very high degree of HM contamination. While HMs in the soils posed low ecological risk, the human health risk assessment indicated potential health effects from Co, particularly in children. The presence of HMs in the soils was noted to originate from both natural geological phenomena and human activities, including industrial operations, agricultural practices, landfill activities, and vehicular emissions.

Assessment of Heavy Metal Contamination and Ecological Risk in Soil within the Zheng–Bian–Luo Urban Agglomeration

As urbanization accelerates, the contamination of urban soil and the consequent health implications stemming from urban expansion are increasingly salient. In recent years, a plethora of cities and regions nationwide have embarked on rigorous soil geological surveys with a focus on environmental quality, yielding invaluable foundational data. This research aims to develop scientifically robust and rational land-use planning strategies while assessing the levels of heavy metal pollution and associated risks. The urban agglomeration encompassing Zhengzhou, Luoyang, and Kaifeng (referred to as Zheng–Bian–Luo Urban Agglomeration) in Henan Province was designated as the study area. Leveraging the Nemerow comprehensive index method alongside the Hakanson potential ecological risk assessment method, this study delved into the pollution levels and potential ecological ramifications of nine heavy metals, namely Cr, Mn, Ni, Cu, Zn, As, Cd, Pb, and Co. Research indicates that the hierarchy of individual potential ecological risks ranges from most to least significant as follows: Cd > Pb > Cr > Ni > Cu > Zn > As > Mn > Co. The concentrations of Cd in both Zhengzhou and Kaifeng surpassed the established background levels. Furthermore, the mean single-factor pollution index values for the heavy metals Cd and Zn exceeded 1, signifying a state of minor pollution. The Nemerow comprehensive index P of Cd and Zn is between 1 < Pcomp ≤ 2, which is considered mild pollution. The comprehensive P values of the other seven metal elements are all less than 0.7, reaching a clean (alert) level. Predominantly, the primary potential risk factor in the superficial soil of the Zheng–Bian–Luo urban agglomeration is Cd, while the ecological risk implications associated with other heavy metal elements are comparatively minimal. The soil environmental quality within the designated study area remains secure, although certain localized areas pose potential risks of heavy metal pollution. A comprehensive assessment of the current state of soil heavy metal pollution is essential to establish a theoretical foundation and provide technical support for soil environmental protection, pollution mitigation, and sustainable utilization.

Indoor air quality prediction modeling for a naturally ventilated fitness building using RNN-LSTM artificial neural networks

PurposeExposure to indoor air pollutants poses a significant health risk, contributing to various ailments such as respiratory and cardiovascular diseases. These unhealthy consequences are specifically alarming for athletes during exercise due to their higher respiratory rate. Therefore, studying, predicting and curtailing exposure to indoor air contaminants during athletic activities is essential for fitness facilities. The objective of this study is to develop a neural network model designed for predicting optimal (in terms of health) occupancy intervals using monitored indoor air quality (IAQ) data.Design/methodology/approachThis research study presents an innovative approach employing a long short-term memory (LSTM) recurrent neural network (RNN) to determine optimal occupancy intervals for ensuring the safety and well-being of occupants. The dataset was collected over a 3-month monitoring campaign, encompassing 15 meteorological and indoor environmental parameters monitored. All the parameters were monitored in 5-min intervals, resulting in a total of 77,520 data points. The dataset collection parameters included the building’s ventilation methods as well as the level of occupancy. Initial preprocessing involved computing the correlation matrix and identifying highly correlated variables to serve as inputs for the LSTM network model.FindingsThe findings underscore the efficacy of the proposed artificial intelligence model in forecasting indoor conditions, yielding highly specific predicted time slots. Using the training dataset and established threshold values, the model effectively identifies benign periods for occupancy. Validation of the predicted time slots is conducted utilizing features chosen from the correlation matrix and their corresponding standard ranges. Essentially, this process determines the ratio of recommended to non-recommended timing intervals.Originality/valueHumans do not have the capacity to process this data and make such a relevant decision, though the complexity of the parameters of IAQ imposes significant barriers to human decision-making, artificial intelligence and machine learning systems, which are different. Present research utilizing multilayer perceptron (MLP) and LSTM algorithms for evaluating indoor air pollution levels lacks the capability to predict specific time slots. This study aims to fill this gap in evaluation methodologies. Therefore, the utilized LSTM-RNN model can provide a day-ahead prediction of indoor air pollutants, making its competency far beyond the human being’s and regular sensors' capacities.

Sustainability in Action: Green Infrastructure as a Marker of Sustainable Urban Development

The current form and pace of urbanization is an ongoing threat to sustainable urban development. As these problems span social, environmental, and economic realms, it is imperative that any proposed solution is able to address these problems holistically. Although a number of silver-bullet solutions such as electric vehicle adoption have been introduced, they do not respond to the problems with an integrated approach. Tackling this problem will require a solution that can be changed and applied on a case-by-case basis. Therefore, the development of green infrastructure is the most efficient solution when responding to the problems threatening urban sustainability. The purpose of this paper is to argue for the efficacy of green infrastructure methods by analyzing the evidence provided about factors like temperature and pollution levels in green cities as compared to other proposed solutions, and to explore critiques of green infrastructure and discuss their potential improvements. The research provided contains a mixture of case studies, participant surveys, and other scientific findings and has the potential to make green infrastructure a priority in the future planning of cities.

Diurnal rail noise measurement, analysis, and evaluation of associated health impacts on residents living in the proximity of rail track area.

In India, railway is the major transportation mode for carrying goods and people. The tracks for the movement of the rail were initially constructed in the city for the pre-eminence and expediency of the vantage of the people. Rapid modernization and increasing population in the city crammed the area around the railway tracks. Moving rail on the tracks passing through the city is not compatible, which is creating problems for the nearby residents. In the urban and suburban regions, the railway noise has become a major problem. This study was conducted to examine the perception of the physiological and psychological effects of railway noise in the nearby areas of railway stations in Delhi, India. For this purpose, 10 sites near the railway station were selected for the study. To assess the impact of railway noise pollution on the health of humans, a questionnaire survey was conducted. The data of 344 individuals were collected through the questionnaire survey and analyzed to get the perception towards railway noise. Noise level was monitored by a Sound Level Meter (SLM) and the equivalent noise level (Leq) in dB(A) was used to compute the noise pollution in three shifts, i.e., morning, noon, and evening time. Results showed that 57.65% of female and 86.11% of male respondents in the survey reported the disturbance due to railway noise. The level of noise pollution was found higher in the evening time as compared to the noon and morningperiod, which exceeds the limit set by the Central Pollution Control Board (CPCB) at all the monitored locations. Findings of the study show that the primary cause of the health problems is railroad noise, which is negatively impacting the health of the residents, who are living in the proximity of the rail track region. The perception survey reported that headache, sleep disturbance, irritation, and stress are common health issues among the locals residing around the railway track proximity in Delhi.

Bridging senses of place and mobilities scholarships to inform social-ecological systems governance: A research agenda

Uncertainty and change are increasingly commonplace as communities respond to impacts of social-ecological change including climate change, and dangerous levels of pollution. Given the extent of these crises, new approaches are needed to support responses. Here we identify challenges and discuss insights that the nexus of Senses of place (SoP) and mobilities research offers in navigating such uncertainty. We conducted a two-round Delphi, followed by a workshop, and collaborative writing process with a global network of researchers with expertise in either or both SoP and mobilities research. Participants identified five challenges at the place-mobility nexus that emerge when a social-ecological system is disrupted. We use the 2022 Odra River fish die-off to exemplify the identified challenges: 1) accounting for power dynamics, inequalities and motility; 2) doing justice to more-than human actors; 3) integrating multiple and sometimes nested spatial scales; 4) considering temporalities of place and mobilities, and 5) embracing multisensoriality. To address these challenges, we recommend drawing on diverse methods and knowledge co-creation processes that combine more-than-human perspectives, multisensoriality, and engage in the dynamic relations between places to understand people-place disruptions in the face of socio-spatial precarity. Addressing such knowledge gaps requires stronger collaboration of mobilities and place researchers.