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In order to reduce the health impacts of air pollution effectively, developing strategies that involves individual or community level is crucial. The purpose of this paper is to assess people’s protective practices for inhalable particulate matter and its significant determinants such as general characteristics, knowledge and attitude among residents of an urban residential area, Dhaka, Bangladesh.

This cross-sectional study was conducted by systematic random sampling. A total of 424 people, who lived in that area for not less than two years before the survey, were interviewed using a structured questionnaire. χ² and Fisher’s exact test were used to analyze the data.

Only a small proportion of respondents had high practice level. In addition, a little more than half has high level of knowledge about inhalable particulate matter, its adverse health effects and protective practices and almost 70 percent had high level of attitude toward air pollution. The protective practices for small inhalable particulate matter was significantly associated with age, educational level, occupation, knowledge and attitude toward small inhalable particulate matter, its adverse health effects and protective measures.

A good level of knowledge about the prevailing air pollution and related health risks can be crucial to develop more focused attempt at changing the current situation with public participation. The environmental experts and health volunteer should disseminate precise and adequate information about long-term health hazards of particulate matter and measures of exposure prevention to improve the protective practices.

This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO₂), ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), as well as particulate matter (PM) (PM₁, PM_2.5 and PM₁₀) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations.

The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out.

CO₂ (430.34 ppm), NO₂ (93.31 ppb), O₃ (19.94 ppb), SO₂ (40.87 ppb), PM₁ (29.31 µg/m3), PM_2.5 (43.56 µg/m³), PM₁₀ (50.70 µg/m³), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO₂ (18%) and low PM₁₀ (49%) contributions, NO₂ and PM₁₀ were the most common pollutants in all of the months. The mean air quality index (AQI) for NO₂ indicated that the AQI was “moderate” (51–100). In contrast to SO₂, whose AQI ranged from “moderate” to “very unhealthy,” O₃'s AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM₁, PM_2.5 and PM₁₀ made up the majority of PC1’s contribution, both PM_2.5 and PM₁₀ were deemed “hazardous.”

The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs.

Lack of literature in terms of indoor air quality (IAQ) in Akure, Ondo State. With this work, the information obtained will assist all stakeholders in policy formulation and implementation. Again, the low-cost sensor used is new to this part of the world.

Ports create harmful effects on their adjacent population because ships discharge noxious gases like SO_X, NO_X, and particulate matter (PM). To tackle this problem, some ports started to control emission through regulations such as Emission Control Areas (ECA) and Reduced Speed Zone (RSZ). This paper estimates the social cost of ship emission and eco-efficiency at the Port of Incheon (POI). We further examine how the ECA and RSZ designation can reduce the social cost. The estimation is based on the activity-based approach, where ship type, engine, and movement are used to measure fuel consumption and then emission. Results suggest that the social cost of ship emission at the POI amounts to $90,805,478. The eco-efficiency of the POI, compared to the one at the Port of Las Palmas in another study, is substantially better. Under RSZ, the corresponding emission abatement values are $4,485,308, $2,642,009 and $21,932,435 from SO₂, NO_X and PM reduction, respectively. If 1.0% and 0.1% sulfur fuel are used complying with rules of the ECA, the social cost savings amount to $8,174,947 and $12,868,842 from SO₂ reduction.

This paper presents a case study examining the emissions impacts of a modal shift from on-road trucks to rail for goods movement through the Southern California ports region, one of the severest non-attainment areas in terms of national air quality standards. Recent completion of the Alameda Corridor, a 20-mile rail expressway that connects the Ports of Long Beach and Los Angeles with rail mainlines near downtown Los Angeles, provides substantial reserve capacity for port traffic to be diverted from the severely congested road network to the rail line. On-road vehicles emissions were estimated using California mobile-source emissions model, ‘EMFAC’ that incorporates a set of emissions factors for each vehicle type and an estimate of vehicle activity. These emissions were then compared with the emissions generated from trains increased to carry freight volume diverted from truck traffic. On the basis of year 2000 traffic level, it was estimated that for a 20 percent modal shift of port traffic, mobile-source emissions can be reduced up to 0.86 tons for nitrogen oxides and 16 kg for particulate matter per day. The analysis results indicate that encouraging the modal shift for port-related freight traffic should be an integral part of overall air quality improvement initiatives for the study area.

Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.