Particulate matter concentration and health risk assessment for a residential building during COVID-19 pandemic in Abha, Saudi Arabia

Elemental composition and health risk assessment of PM10, PM2.5, at different microenvironments: Addis Ababa, Ethiopia

This study was designed to evaluate the health risks faced by inhabitants living in the slum areas of Addis Ababa, Ethiopia. The levels of PM2.5 and PM10 and elemental composition of the PM10 were measured in indoors (in the kitchen and living room) and outdoors (at the roadside). A total of 75 sampling locations (45 indoor and 30 outdoor) were selected for the study. The levels of PM2.5 and PM10 were determined using an AROCET531S instrument, while an universal air pump was used for the sampling of PM10 for the determination of trace elements by inductively coupled plasma-optical emission spectroscopy (ICP‒OES). The health impacts of PMs on the inhabitants of twelve microenvironments (MEs), where they spend much of their daily time, were estimated. The total amounts of PM2.5 and PM10, and trace metals in PM10 found in the nine or twelve MEs ranged from 10.6-119, 128-185, and 0.007-0.197 μg m-3, respectively. According to the United States Environment Protection Agency (USEPA) guidelines, ten of the twelve MEs can cause significant health problems for inhabitants (HI > 1) due to PM2.5 and PM10. Thus, special attention should be given by stakeholders/inhabitants to minimize the health impacts on long-term exposure. This study assessed the risk of levels of trace elements on the inhabitants who spend most of their daily lives. The study revealed that the lifetime cancer risk values for the individual and cumulative trace elements were within the tolerable range set by the USEPA guidelines.

Viral infection transmission and indoor air quality: A systematic review

Respiratory disease transmission in indoor environments presents persistent challenges for health authorities, as exemplified by the recent COVID-19 pandemic. This underscores the urgent necessity to investigate the dynamics of viral infection transmission within indoor environments. This systematic review delves into the methodologies of respiratory infection transmission in indoor settings and explores how the quality of indoor air (IAQ) can be controlled to alleviate this risk while considering the imperative of sustainability. Among the 2722 articles reviewed, 178 were retained based on their focus on respiratory viral infection transmission and IAQ. Fifty eight articles delved into SARS-CoV-2 transmission, 21 papers evaluated IAQ in contexts of other pandemics, 53 papers assessed IAQ during the SARS-CoV-2 pandemic, and 46 papers examined control strategies to mitigate infectious transmission. Furthermore, of the 46 papers investigating control strategies, only nine considered energy consumption. These findings highlight clear gaps in current research, such as analyzing indoor air and surface samples for specific indoor environments, oversight of indoor and outdoor parameters (e.g., temperature, relative humidity (RH), and building orientation), neglect of occupancy schedules, and the absence of considerations for energy consumption while enhancing IAQ. This study distinctly identifies the indoor environmental conditions conducive to the thriving of each respiratory virus, offering IAQ trade-offs to mitigate the risk of dominant viruses at any given time. This study argues that future research should involve digital twins in conjunction with machine learning (ML) techniques. This approach aims to enhance IAQ by analyzing the transmission patterns of various respiratory viruses while considering energy consumption.

Satellite or ground-based measurements for air pollutants (PM2.5, PM10, SO2, NO2, O3) data and their health hazards: which is most accurate and why?

Air pollution is growing at alarming rates on regional and global levels, with significant consequences for human health, ecosystems, and change in climatic conditions. The present 12 weeks (4 October 2021, to 26 December 2021) study revealed the different ambient air quality parameters, i.e., PM2.5, PM10, SO2, NO2, and O3 over four different sampling stations of Delhi-NCR region (Dwarka, Knowledge park III, Sector 125, and Vivek Vihar), India, by using satellite remote sensing data (MERRA-2, OMI, and Aura Satellite) and different ground-based instruments. The ground-based observation revealed the mean concentration of PM2.5 in Dwarka, Knowledge park III, Sector 125, and Vivek Vihar as 279 µg m-3, 274 µg m-3, 294 µg m-3, and 365 µg m-3, respectively. The ground-based instrumental concentration of PM2.5 was greater than that of satellite observations, while as for SO2 and NO2, the mean concentration of satellite-based monitoring was higher as compared to other contaminants. Negative and positive correlations were observed among particulate matter, trace gases, and various meteorological parameters. The wind direction proved to be one of the prominent parameter to alter the variation of these pollutants. The current study provides a perception into an observable behavior of particulate matter, trace gases, their variation with meteorological parameters, their health hazards, and the gap between the measurements of satellite remote sensing and ground-based measurements.

Assessment of ambient air quality in heavy industrial localities: a case study of Yanbu industrial city

The objective of this study was to provide an appropriate evaluation of ambient air quality in industrial localities and the surrounding residential areas in its vicinity. Therefore, an assessment of gaseous emissions from industrial sectors was performed. For this purpose, concentrations were measured for SO2, H2S, NO2, O3, CO, PM2.5, and PM10 in five spatially diverse monitoring stations (AQMS) over different temporal intervals (daily, monthly, and annual) for the years 2015-2020. The impact on the environment and public health was evaluated through comparison with the corresponding regional and international standards. In the case study region, a substantial spatiotemporal variation was observed in the gaseous contaminants, due to the predominance of characterized meteorological parameters interfering with contributions from existing chemical facilities and anthropogenic activities. The exceedances for the investigated emissions were routinely violated the standard concentrations. According to AQI classifications, these violations were assigned to be within the acceptable limits for the gaseous emissions, moderately polluted for PM2.5, and unhealthy for sensitive groups for PM10. The proper distribution of the AQMSs within the industrial locality provides enough spatial and temporal observatory data, such that the exceedances were reduced with the subsequent years, hence appropriate evaluation of the relevant measurements revealed effective qualitative policies taken into action by authorities to maintain less accumulation of the gaseous emissions into ambient air beyond the harmful limits for public health and environment.

A systematic literature review on indoor PM2.5 concentrations and personal exposure in urban residential buildings

Particulate matter with an aerodynamic diameter less than 2.5μm (PM_2.5) is currently a major air pollutant that has been raising public attention. Studies have found that short/long-term exposure to PM_2.5 lead detrimental health effects. Since people in most region of the world spend a large proportion of time in dwellings, personal exposure to PM_2.5 in home microenvironment should be carefully investigated. The objective of this review is to investigate and summary studies in terms of personal exposure to indoor PM_2.5 pollutants from the literature between 2000 and 2021. Factors from both outdoor and indoor environment that have impact on indoor PM_2.5 levels were explicated. Exposure studies were verified relating to individual activity pattern and exposure models. It was found that abundant investigations in terms of personal exposure to indoor PM_2.5 is affected by factors including concentration level, exposure duration and personal diversity. Personal exposure models, including microenvironment model, mathematical model, stochastic model and other simulation models of particle deposition in different regions of human airway are reviewed. Further studies joining indoor measurement and simulation of PM_2.5 concentration and estimation of deposition in human respiratory tract are necessary for individual health protection.

Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning

Air pollution is associated with respiratory diseases and the transmission of infectious diseases. In this context, the association between meteorological factors and poor air quality possibly contributes to the transmission of COVID-19. Therefore, analyzing historical data of particulate matter (PM2.5, and PM10) and meteorological factors in indoor and outdoor environments to discover patterns that allow predicting future confirmed cases of COVID-19 is a challenge within a long pandemic. In this study, a hybrid approach based on machine learning and deep learning is proposed to predict confirmed cases of COVID-19. On the one hand, a clustering algorithm based on K-means allows the discovery of behavior patterns by forming groups with high cohesion. On the other hand, multivariate linear regression is implemented through a long short-term memory (LSTM) neural network, building a reliable predictive model in the training stage. The LSTM prediction model is evaluated through error metrics, achieving the highest performance and accuracy in predicting confirmed cases of COVID-19, using data of PM2.5 and PM10 concentrations and meteorological factors of the outdoor environment. The predictive model obtains a root-mean-square error (RMSE) of 0.0897, mean absolute error (MAE) of 0.0837, and mean absolute percentage error (MAPE) of 0.4229 in the testing stage. When using a dataset of PM2.5, PM10, and meteorological parameters collected inside 20 households from 27 May to 13 October 2021, the highest performance is obtained with an RMSE of 0.0892, MAE of 0.0592, and MAPE of 0.2061 in the testing stage. Moreover, in the validation stage, the predictive model obtains a very acceptable performance with values between 0.4152 and 3.9084 for RMSE, and a MAPE of less than 4.1%, using three different datasets with indoor environment values.

Energy consumption, thermal comfort, and indoor air quality in mosques: Impact of Covid-19 measures

Restrictions have been imposed on the number of people, the duration of their stay and air conditioning operation in temples to limit the spread of the SARS-CoV-2 pandemic. This work studied how restrictions affected energy consumption, thermal comfort, and indoor air quality (IAQ) in mosques. Energy consumption data on lighting, heating and cooling before and during the pandemic were analyzed in six mosques of various sizes located in Yalova city, Turkey. The annual energy consumption for lighting was reduced during the pandemic in all mosques due to less usage, while the annual heating and cooling costs were raised in one mosque despite their restricted use. Besides, experiments were conducted to assess the effect of pandemic measures on thermal comfort and IAQ by measuring indoor temperature, relative humidity, air velocity, CO2 and PM concentrations in a typical mosque. Keeping the windows open and limiting occupancy improved the IAQ. This was evidenced by the lower average CO2 concentration during the pandemic (428 ± 40 ppm) than before the pandemic (661 ± 201 ppm). An acceptable thermal environment was achieved under pandemic measures at night during the summer period. Creating excellent conditions can be difficult without air conditioning even with open windows and prayers performed at night.