Introduction: Each one of us is directly or indirectly exposed to noise pollution in our daily life. Noise has chronic effect on the human but many of us are not aware. In our modern research platform very few studies are available for monitoring and mitigating of noise pollution compared to other environmental pollution.

Materials and methods: This study has been designed to monitor, map the noise pollution in educational institute and find out the sources of noise followed by identification of hot spot. In this regards National Institute of Technology Raipur, Chhattisgarh, India was selected as study area. Noise levels measurements were carried out at 15 locations within the study area at time intervals of forenoon (9:30 – 10:30 AM), noon (12:30-1:30 PM) and afternoon (4:30-5:30 PM) for 5 days of the week (working days). Using GIS tool observed noise levels were interpolated by Inverse Distance Weighted (IDW) method and graphical plots were prepared for different time intervals.

Results: Noise Levels were found to be between 46 dBA to 72.08 dBA during our study. Sources contributing to higher levels of noise in the premises were traffic, honking of trains followed by students themselves. On comparing the finding with Central Pollution Control Board, New Delhi, India (CPCB) standards all the locations recorded higher noise levels than the prescribed limits.

Conclusion: Based on our finding, mitigating approaches like: plantation of trees, construction of noise barriers, proper parking area, restricting high speed of vehicles etc. were suggested for making a healthy learning environment.

Introduction: Global warming and the need to reduce greenhouse gas emissions from various emission sectors are not hidden from anyone. The aim of this study was to determine Carbon dioxide (CO₂)capture
from combustion gases of methane for cultivation of microalgae spirulina platensis.
Materials and methods: Microalgae culture medium was added in two photobioreactor. Air and combustion gas was injected into control and test reactors respectively. Artificial light with 10 Klux intensity was used and
operated in continuous and intermittent (14 h ON and 8 h OFF) modes. Inlet concentration of carbon dioxide in to the test photobiorector was set in the range of 2000 to 6000 ppm and was measured in the inlet and outlet of
photo-bioreactor by ND-IR CO₂ analyzer.
Results: In the control photo-bioreactor, the average removal of CO₂ from the air was 42%. In the test reactor with an inlet CO₂ concentration of 4100 ppm, the average removal of CO₂ from the combustion gas was 23%. After 9 days of cultivation, the amount of carbon dioxide stabilized by microalgae was 0.528 and 1.14 g/L (dry weight) in the control and experimental photobioreactors respectively. The CO2 bio-fixation rate was in the range of 2.2% and 4.0% at different runs. After 9.0 days of cultivation concentration of microalgae was 0.25 and 1.0 g/L in the control and test reactors respectively. Algae productivity with intermittent light was 35% less than continuous light exposure.
Conclusion: It is possible to use CO₂ capture from combustion gases of commercial heater for cultivation of microalgae spirulina.

Introduction: This paper presents a portable device of Air Quality Monitoring System (AQMS) based on a sensor platform. The purpose of the study is to power awareness, warning indication, and minimal data storage capability.
Materials and methods: AQMS is developed by embedded design methodology. The software part is based on the C programming language. AQMS device is made up of “transmitter” and “receiver” sections through
the Zigbee wireless network. The objective is to collect concentrations of CO, NO₂, CO₂, humidity, and temperature to check air pollution for health awareness. A power-saving strategy is adopted in the "transmitter" of AQMS through a Demultiplexer circuit. To minimize the space complexity of storage and availability of long-term data, data encoding techniques are implemented.
Results: By implementing switching activity on the sensors in AQMS, the active mode of sensing operations are controlled and a power saving of 18.41% is achieved. Extending the duration of transmission operation
increases data storage in the “receiver” unit. Hence, two encoding techniques are developed where real-time data are encoded in binary form: 2-bit encoding and 3-bit encoding. According to the results, 2-bit encoding
saves 50% of storage space and 3-bit encoding saves 25%, compared to not utilizing any encoding strategy, sacrificing data accuracy by less than 5%.
Conclusion: AQMS design is created with the implementations of low power consumption and low storage. Additionally, an alarming condition is set in AQMS for indicating the level of pollutants in the air to determine the risk level of exposure which is dangerous for human health.

Introduction: Receptor models use the chemical characterisation of particulate matter to determine the source and analyse the source contributions. The main aim of this study is to carry out source apportionment
of PM₁₀ for industrial locations of Vapi and Ankleshwar in Gujarat, using the Chemical Mass Balance (CMB) receptor model.
Materials and methods: At six distinct locations of Ankleshwar and Vapi, respirable dust samplers were used to collect particulate matter on quartz filter sheets for the current study. Filter papers containing PM₁₀ mass were subsequently examined for Water Soluble Ions (WSIs), major and trace elements, elemental and organic carbon followed by source apportionment study.
Results: Using CMB, the contributions obtained for Ankleshwar are 27.85% for crustal or soil dust, 26.31% for fossil fuel combustion, 21.06% for vehicle emissions, 14.20% for secondary aerosols, 9.30% for biomass, and 1.20% for industrial emissions. CMB for Vapi revealed the chief source signatures as fossil fuel combustion including industries contributing 35%, crustal or soil dust contributing 22.90%, biomass burning contributing
19.12%, vehicular emissions contributing 16.18%, and secondary aerosols contributing 6.79%.
Conclusion: By applying the CMB model, the primary source is found to be crustal or soil dust followed by burning fossil fuels, vehicular emissions, and secondary aerosols for Ankleshwar and Vapi, respectively. A quantitative assessment of source contributions to particulate matter is required to create emission control measures. The findings of this study will be beneficial for the environmental management of particle concentrations in the study region.

Introduction: Road traffic emissions are among the most significant sources of pollution in Douala, Cameroon's economic town, alongside industrial operations. The morning and the evening are two times of the day when traffic is heavier and the winds are also at their calmest. The majority of the non-exhaust Particulate Matters (PMs) produced by autos is re-suspended road contaminants. The purpose of this research is to estimate fine particle dispersion in conditions of calm winds.
Materials and methods: In one of Douala's roundabouts, the Gaussian Plume model is employed to calculate the PM concentration under calm winds conditions. Different vehicle classes (HDV: Heavy Duty Vehicles, LDV: Light Duty Vehicles, PC: Passenger Cars) are used to figure out the amount of PMs they produce. Measurements of ambient fine particle concentrations are made with the OC-300 laser dust particle detector.
Results: The results made it possible to compare actual measurements of PM_2.5, PM₁₀ (300±150 µg/m³ and 650±150 µg/m³ , respectively) with simulated values (PM_2.5, PM₁₀: 310 µg/m³ and 631 µg/m³ , respectively). The difference between in-situ and computed values can range from 10 to 132
µg/m³. From 6 to 10 AM, the population's exposure to PM pollution is more severe. It has also been demonstrated that there is a significant association between traffic flow and PM Concentration during the dry season (R²=0.921). With increased traffic flow intensity, particle concentration levels rise.
Conclusion: The concentration threshold stays above the World Health Organization (WHO) limits regardless of the approach. Furthermore, this paper provides important information about Douala's pollution levels.

Introduction: In various mining activities, workers are exposed to free Crystalline Silica (CS), which can cause the constant production of reactive oxygen species and silicosis. This research was conducted to evaluate oxidative stress biomarkers and liver tissue function in workers occupationally exposed to CS during their activities.

Materials and methods: In this study, the biomarkers of oxidative stress were evaluated in 40 workers in silica mines of Azandarian region (Hamadan province, Iran) with occupational exposure to CS, as the case group and 40 workers without any silica-exposure as controls.

Results: A significant higher serum levels of Malondialdehyde (MDA), Reactive Oxygen Species (ROS) and Alanine Transaminase (ALT) were observed in the silica-exposed group compared to the controls. Moreover, in the serum of the silica-exposed cases, the total antioxidant capacity was lower than that of the control group. Based on findings chronic exposure to CS can obviously affect the serum levels of oxidative stress biomarker and liver tissue function in the exposed workers.

Conclusion: Therefore, the use of suitable face mask and different dietary antioxidants are recommended in the silica-exposed workers for the reduction of oxidative stress production and prevention of liver tissue disorders.

Introduction: Rapid urbanization and industrial growth are the primary causes of deteriorating urban air quality in developing countries, including India. Vehicular emission is a significant cause of the degradation of air quality. It includes Air Pollution like carbon monoxide, hydrocarbons, oxides of nitrogen, oxides of sulfur, Suspended Particulate Matter (SPM), respiratory Particulate Matters (PM_2.5 and PM₁₀), and lead.
Materials and methods: The study has considered land use,land cover, land surface temperature, vegetation, literacy rate, vehicle population, population density, and households, finding the responsible causes of air pollutionfor the analysis. Supervised classification using ArcGIS for extracting land use and land cover, Least Slack Time (LST) algorithms have used to extract land surface temperature, spatial interpolation methods like
Inverse Distance Weighting (IDW) using ArcGIS for identifying the spatial distribution of Literacy rate, vehicle population, population density and households and finally the multivariate statistical model applied to identify the major responsible factor for air pollution using SPSS.
Results: The study reveals that the particulate matter is below the standard value prescribed by the central pollution control board. The Highest air pollution is primarily responsible for vehicle population and industries.
Wednesday and Thursday are the maximum pollution in Chennai, whereas it was recorded as very low on Sunday as compared to other days.
Conclusion: Regression shows that the vehicle population is responsible for air pollution, followed by the population.

Acute Respiratory Infection (ARI) is an upper or lower respiratory tract disease, which can be contagious and can cause a wide spectrum of diseases ranging from asymptomatic disease or mild infection to severe and fatal disease, it depending on the causative pathogen and home environmental factors which affect it. This study aims to analyze how the relationship between home environmental conditions including ventilation, humidity, floors, residential density, and smoking habits with the incidence of Acute Respiratory Infection (ARI) in Toddlers in Indonesia by conducting a meta-analysis on data from various research articles. The method in this study is a meta-analysis by finding the effect size value using JASP software. Articles performed a meta-analysis of 25 articles. The results of the meta-analysis found that the variable density of residential has 1,135 times larger, 1,665 times greater ventilation, ventilation of 1.568 times greater, and the floor conditions 1,309 times larger, as well as the habit sapped. The conclusion from the results of this study shows that the condition of the home environment that has the most influence is the humidity of the house and the one with the lowest risk is residential density. Suggestions for controlling risk and providing education to the community and assistance for healthy homes.