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In recent years, the rising levels of atmospheric particulate matter (PM) have an impact on the earth’s system, leading to undesirable consequences on various aspects like human health, visibility, and climate. The present work is carried out over an insufficiently studied but polluted urban area of Peshawar, which lies at the foothills of the famous Himalaya and Karakorum area, Northern Pakistan. The particulate matter with an aerodynamic diameter of less than 10 µm, i.e., PM 10 are collected and analyzed for mineralogical, morphological, and chemical properties. Diverse techniques were used to examine the PM 10 samples, for instance, Fourier transform infrared spectroscopy, x-ray diffraction, and scanning electron microscopy along with energy-dispersive x-ray spectroscopy, proton-induced x-ray emission, and an OC/EC carbon analyzer. The 24 h average PM 10 mass concentration along with standard deviation was investigated to be 586.83 ± 217.70 µg/m 3 , which was around 13 times greater than the permissible limit of the world health organization (45 µg/m 3 ) and 4 times the Pakistan national environmental quality standards for ambient PM 10 (150 µg/m 3 ). Minerals such as crystalline silicate, carbonate, asbestiform minerals, sulfate, and clay minerals were found using FTIR and XRD investigations. Microscopic examination revealed particles of various shapes, including angular, flaky, rod-like, crystalline, irregular, rounded, porous, chain, spherical, and agglomeration structures. This proved that the particles had geogenic, anthropogenic, and biological origins. The average value of organic carbon, elemental carbon, and total carbon is found to be 91.56 ± 43.17, 6.72 ± 1.99, and 102.41 ± 44.90 µg/m 3 , respectively. Water-soluble ions K + and OC show a substantial association ( R  = 0.71). Prominent sources identified using Principle component analysis (PCA) are anthropogenic, crustal, industrial, and electronic combustion. This research paper identified the potential sources of PM 10 , which are vital for preparing an air quality management plan in the urban environment of Peshawar.

This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems. The air quality sensor is designed at a minimum cost and complexity to allow multi-zone implementation without significant effort. The user interface uses a spatial graphic representation that facilitates understanding areas with different air quality levels. Presentation of the outdoor air quality level supports the user’s decision to ventilate a space. An innovative element of the proposed monitoring interface is the real-time forecast of air quality evolution in each monitored space. The paper describes the implementation of an original monitoring solution (monitoring device, Edge/Cloud management system, innovative user monitoring interface) and presents the results of testing this system in a relevant environment. The research conclusions show the proposed solution’s benefits in improving the end-user experience, justified both by the technical results obtained and by the opinion of the users who tested the monitoring system.

The National Railway Master Plan, it is stated that the target of developing the railway network in South Sulawesi Province is to connect areas that have the potential for transporting passengers and goods to support the development of integration between districts. The construction of the railway line has the potential to reduce air quality and health risks to the community around the location. This study aims to assess air quality and its risks during the construction of the railway line from Makassar to Parepare as a reference for environmental management and monitoring plan documents. Air sampling was made using multiple impinger and dust with a hi-volt dust sampler and then analyzed in the laboratory and compared with the Air Pollution Standard Index. Analysis of potential pollutants on health was carried out using the Environmental Health Risk Assessment method. The results showed that the air quality at the time of the study was still below the threshold value, and the environmental health risk assessment was still below the value with RQ > 0.1 except for SO2 in adults. The conclusion of the study shows that the air quality at the time of the construction of the railway line is still relatively good, and environmental management and monitoring have been carried out quite well based on the direction of the environmental management and monitoring plan including implementing a green open space management program.

Analysis and prediction on real time air quality data is a critical step in solving various problems related to pollution and finding a genuine solution. However, missing values in air pollution data is a serious issue that may greatly influence the performance of such analysis and prediction. In order to address this problem, a 2-step process is proposed, consisting of a data pre-processing stage using Q-FUZZY (Quantized Fuzzification) model and data imputation stage via a Fuzzy Imputation Model(FIM) to handle this complexity. We have validated the proposed approach using a real world dataset of Kolkata, India containing pollution levels of different air pollutants along with meteorological parameters. Various performance measures are used to determine the effectiveness of FIM. Moreover, the performance of the proposed model is also compared with the baselines namely, FLAR (Fuzzy Least Absolute Regression), ridge regression and EMD-SVR-SARIMA (Empirical Mode Decomposition-Support Vector Regressions-Seasonal Autoregressive Integrated Moving Average). On comparison, it is observed that FIM achieves an overall better performance in terms of distance measures, Mean Similarity Measures (MSM), Mean Inclusion Measures (MIM) and Mean Predictive Ability (MPA).

We have investigated the impact of reduced emissions due to COVID-19 lockdown measures in spring 2020 on air quality in Canada’s four largest cities: Toronto, Montreal, Vancouver, and Calgary. Observed daily concentrations of NO2, PM2.5, and O3 during a “pre-lockdown” period (15 February–14 March 2020) and a “lockdown” period (22 March–2 May 2020), when lockdown measures were in full force everywhere in Canada, were compared to the same periods in the previous decade (2010–2019). Higher-than-usual seasonal declines in mean daily NO2 were observed for the pre-lockdown to lockdown periods in 2020. For PM2.5, Montreal was the only city with a higher-than-usual seasonal decline, whereas for O3 all four cities remained within the previous decadal range. In order to isolate the impact of lockdown-related emission changes from other factors such as seasonal changes in meteorology and emissions and meteorological variability, two emission scenarios were performed with the GEM-MACH air quality model. The first was a Business-As-Usual (BAU) scenario with baseline emissions and the second was a more realistic simulation with estimated COVID-19 lockdown emissions. NO2 surface concentrations for the COVID-19 emission scenario decreased by 31 to 34% on average relative to the BAU scenario in the four metropolitan areas. Lower decreases ranging from 6 to 17% were predicted for PM2.5. O3 surface concentrations, on the other hand, showed increases up to a maximum of 21% close to city centers versus slight decreases over the suburbs, but Ox (odd oxygen), like NO2 and PM2.5, decreased as expected over these cities.

Sydney Metro is the biggest project of Australia’s public transport, which was designed to provide passengers with more trains and faster services. This project was first implemented in 2017 and is planned to be completed in 2024. As presented, the project is currently in the construction stage located on the ground stations of the Sydney Trains Bankstown line (T3). Based on this stage, several construction activities will generate air pollutants, which will affect the air quality around construction areas. Moreover, it might cause health problems to people around there and also the passengers who usually take the train on the T3 line. However, there is no specific data for air quality inside the train that may be affected by the construction from each area. Therefore, the aim of this study is to investigate the air quality inside the train carriage of all related stations from the T3 line. A sampling campaign was conducted over 3 months to analyze particulate matter (PM) concentration, the main indoor pollutants including formaldehyde (HCHO) and total volatile organic compounds (TVOC). The results of the T3 line were analyzed and compared to Airport & South line (T8) that were not affected by the project’s construction. The results of this study indicate that Sydney Metro construction activities insignificantly affected the air quality inside the train. Average PM2.5 and PM10 inside the train of T3 line in the daytime were slightly higher than in the nighttime. The differences in PM2.5 and PM10 concentrations from these periods were around 6.8 μg/m3 and 12.1 μg/m3, respectively. The PM concentrations inside the train from the T3 line were slightly higher than the T8 line. However, these concentrations were still lower than those recommended by the national air quality standards. For HCHO and TVOC, the average HCHO and TVOC concentrations were less than the recommendation criteria.

Air pollution (O3, SO2, CO, NOx, PM, and Pb) can have a severe impact on environmental damage and human health problems. Therefore, monitoring of pollutant levels in the air is essential to find out how much that gas levels can cause air pollution. In general, air quality monitoring is carried out using a conventional system. Inside the system requires large-sized equipment, long time for analysis, expensive, and with limited space resolution. It becomes inefficient, amid the development of technology that can carry out online monitoring, real-time, low cost, small size and a wide range of distance. The technology is known as the wireless sensor network system. The aim of this research is to design an air quality monitoring system using a Wireless Sensor Network (WSN) that can be accessed via the web and smartphone. The developing of WSN systems consist of two steps, are design and implementation device. The first step is done by assembling sensor nodes and hosting a web server. And after the device was finished, the next step is testing and calibrating of the system. The results of experiment show that the system is able to detect various air pollution, such as SO2, NOX, CO, and other environmental factors such as temperature, humidity and wind speed.

Research on air quality analysis is a hot field. Here we describe an analysis process based on cluster methods for the data of ambient air quality. In this paper, we use the process to cluster on the air quality data which from the National Urban Air Quality Report in December 2020 on the official website of the Ministry of Ecology and Environment of the People’s Republic of China. We find that cities in different clusters with different main pollutants and pollution levels. Ambient air quality analysis aims to provide guidance for reducing the impact of air pollution on health.

This paper presents the design philosophy, set-up, and calibration methodology of full-scale in- situ test cells. The main feature and key aspect of this approach is based on experimental twin cells called HYBCELL at ENTPE. The experimental calibration follows European construction standards and involves adjusting the sensors, instrumentation, and operating parameters in both cells to ensure a uniform baseline, using a variety of sensors within our setup. These sensors record various measurements to assess thermal comfort, indoor air quality, boundary conditions, system performance, envelope efficiency, and occupancy simulation. This study aims to analyse the behaviour of both cells under identical conditions by testing and comparing the functioning of each room. This ensures their symmetry. This approach aims to provide more accurate results when designing the envelope and HVAC systems, leading to characterise and study indoor air conditions and validate numerical models. The calibration was performed with four scenarios, with activated and deactivated HVAC systems. In addition, air and ventilation system calibrations are realised using indoor and outdoor CO2 and particle concentration measurements to determine the air renewal rates in each room. renewal rates in each room. The calibration feedback was analysed, and the obtained results allowed to calibrate the twin cells and to define recommendations for their use.