scholarly journals A Systematic Review on School Air Quality and Its Impact on Student’s Health in Tropical Countries

2020 ◽  
Vol 12 (11) ◽  
pp. 20
Author(s):  
Bambang Wispriyono ◽  
Budi Hartono ◽  
Ririn Arminsih Wulandari ◽  
Sasnila Pakpahan ◽  
Gita Permata Aryati ◽  
...  
Keyword(s):  
Air Quality ◽  
Environmental Factors ◽  
School Environment ◽  
Air Pollutants ◽  
Negative Impact ◽  
Case Study Research ◽  
Health Impact ◽  
Common Source ◽  
Tropical Country ◽  
Tropical Countries

Poor air quality in school areas has a negative impact on the student’s health. Several studies in tropical countries have reported the risk exposure and environmental factors that were associated to the air quality in school areas. This paper presents a review of several case study research associated to air pollutants and environmental factors on the surrounding school environment and the health impact on students in tropical country. We selected and reviewed 18 research papers related to air quality in schools. The selection method was based on the inclusion and exclusion criteria. Throughout these studies, the most common source of air pollutants found in the classroom was particulate matter. Air quality in schools is affected by the distance between the school and the source of pollutants, ventilation, inhabitant, and season. Exposure to poor indoor air quality can increase health risk, respiratory problems, ocular problems, and students’ absence from school.

scholarly journals Quantifying the Health Benefits of Urban Climate Mitigation Actions: Current State of the Epidemiological Evidence and Application in Health Impact Assessments

2021 ◽  
Vol 3 ◽  
Author(s):  
Maria D. Castillo ◽  
Susan C. Anenberg ◽  
Zoe A. Chafe ◽  
Rachel Huxley ◽  
Lauren S. Johnson ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Health Benefits ◽  
Health Impact ◽  
Epidemiological Studies ◽  
Health Improvement ◽  
Current State ◽  
The World ◽  
Climate Action ◽  
Health Impact Assessments

While ambitious carbon reduction policies are needed to avoid dangerous levels of climate change, the costs of these policies can be balanced by wide ranging health benefits for local communities. Cities, responsible for ~70% of the world's greenhouse gas (GHG) emissions and home to a growing majority of the world's population, offer enormous opportunities for both climate action and health improvement. We aim to review the current state of knowledge on key pathways leading from carbon mitigation to human health benefits, and to evaluate our current ability to quantify health benefits for cities around the world. For example, because GHGs and air pollutants are both released during fuel combustion, reducing fuel burning can reduce both GHGs and air pollutants, leading to direct health benefits. Air quality improvements may be particularly important for city-scale climate action planning because the benefits occur locally and relatively immediately, compared with the global and long-term (typically, decades to centuries) benefits for the climate system. In addition to improved air quality, actions that promote active transport in cities via improved cycling and pedestrian infrastructure can reap large cardiovascular health benefits via increased physical activity. Exposure to green space has been associated with beneficial health outcomes in a growing number of epidemiological studies and meta-analyses conducted around the world. Finally, noise is an underappreciated environmental risk factor in cities which can be addressed through actions to reduce motor vehicle traffic and other noise sources. All of these environmental health pathways are supported by well-conducted epidemiological studies in multiple locales, providing quantitative exposure–response data that can be used as inputs to health impact assessments (HIAs). However, most epidemiologic evidence derives from studies in high-income countries. It is unclear to what extent such evidence is directly transferable for policies in low- and middle-income countries (LMICs). This gap calls for a future focus on building the evidence based in LMIC cities. Finally, the literature suggests that policies are likely to be most effective when they are developed by multidisciplinary teams that include policy makers, researchers, and representatives from affected communities.

scholarly journals Comparative study on air quality status in Golden Quadrilateral cities before and during the COVID-19 lock down period.

2021 ◽  
Vol 23 (06) ◽  
pp. 1-10
Author(s):  
Geeta Singh ◽  
◽  
Ayushi Jha ◽  
Rashmi Kumari ◽  
Vishal Kumar Singh ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
World Economy ◽  
Meteorological Parameters ◽  
Premature Mortality ◽  
Health Impact ◽  
Human Action ◽  
Quality Parameters ◽  
Pollution Level

The COVID-19 pandemics have affected every aspect of the human race and the world economy. The disease has been contaminated in almost every part of India. A threat for poor standards induced premature mortality from cardiovascular disease and respiratory diseases. Amongst the huge-reaching implications of the continuing COVID-19 outbreak, a significant enhancement in air quality was detected all around the globe after lockdowns enforced in several cities in India. The lockdown influenced the environment’s pollution level and improved air quality quickly due to very few human activities. The present work scientifically analyses the air pollutants (PM2.5, PM10, NO2, and SO2) with meteorological parameters in the golden quadrilateral cities. The purpose of this paper is to review the analysis of air quality of golden quadrilateral cities (Delhi, Kolkata, Chennai, and Mumbai). Data of air quality parameters are collectively taken from different locations from different regions of Delhi, Kolkata, Chennai, and Mumbai before lockdown and during the lockdown and compared the data of both periods. Comparison pre-lockdown and 2019 with respect to lockdown and 2020 respectively show a huge reduction in amounts of pollutants. Our objective is to find the implication of different lockdown measures on air quality levels in Delhi, Kolkata, Chennai, and Mumbai particularly this investigation is focused on PM2.5, PM10, NO2, SO2 which is directly transmitted by human action and formed through a chemical reaction in the atmosphere as well as quantify the short-range and long-range health impact.

scholarly journals COVID-19 and Environment

2020 ◽  
Vol 1 (supplement) ◽  
pp. 4
Author(s):  
Mahwish Ali
Keyword(s):  
Air Quality ◽  
Mortality Rate ◽  
Environmental Factors ◽  
Air Pollutants ◽  
Quality Index ◽  
Air Quality Index ◽  
Contaminated Water ◽  
Stool Samples ◽  
Potential Impact ◽  
Transmission Of Disease

Environmental and demographic factors played an important role in the transmission of COVID-19,. This review discusses the potential impact of climatic variables such as temperature, humidity, Air Quality Index (AQI), air pollutants, wastewater and different surfaces on the spread of SARS-COVID-2 virus. Different studies have demonstrated the significant effect of ambient rise in temperature and humidity on Corona cases. However, air quality index and air pollutants are more significantly associated with the mortality rate of corona patients. Furthermore, COVID-19 can survive longer on smooth surfaces as compare to the rough surfaces. The presence of the virus is also detected in stool samples of the patient and wastewater but no study has shown transmission of disease through drinking contaminated water. Hence, meteorological and environmental factors have significant impact on the occurrence of the virus and its spread.

scholarly journals Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China

2019 ◽  
Vol 16 (6) ◽  
pp. 1014 ◽  
Author(s):  
Shaobo Zhong ◽  
Zhichen Yu ◽  
Wei Zhu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Human Health ◽  
Air Pollutants ◽  
Pearson Correlation ◽  
Additive Model ◽  
Health Impact ◽  
Cerebrovascular Diseases ◽  
Relative Risks ◽  
The Impact

There is an increasing body of evidence showing the impact of air pollutants on human health such as on the respiratory, and cardio- and cerebrovascular systems. In China, as people begin to pay more attention to air quality, recent research focused on the quantitative assessment of the effects of air pollutants on human health. To assess the health effects of air pollutants and to construct an indicator placing emphasis on health impact, a generalized additive model was selected to assess the health burden caused by air pollution. We obtained Baidu indices (an evaluation indicator launched by Baidu Corporation to reflect the search popularity of keywords from its search engine) to assess daily query frequencies of 25 keywords considered associated with air pollution-related diseases. Moreover, we also calculated the daily concentrations of major air pollutants (including PM10, PM2.5, SO2, O3, NO2, and CO) and the daily air quality index (AQI) values, and three meteorological factors: daily mean wind level, daily mean air temperature, and daily mean relative humidity. These data cover the area of Beijing from 1 March 2015 to 30 April 2017. Through the analysis, we produced the relative risks (RRs) of the six main air pollutants for respiratory, and cardio- and cerebrovascular diseases. The results showed that O3 and NO2 have the highest health impact, followed by PM10 and PM2.5. The effects of any pollutant on cardiovascular diseases was consistently higher than on respiratory diseases. Furthermore, we evaluated the currently used AQI in China and proposed an RR-based index (health AQI, HAQI) that is intended for better indicating the effects of air pollutants on respiratory, and cardio- and cerebrovascular diseases than AQI. A higher Pearson correlation coefficient between HAQI and RRTotal than that between AQI and RRTotal endorsed our efforts.

scholarly journals A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC

2021 ◽  
Vol 13 (2) ◽  
pp. 529-570
Author(s):  
Lei Kong ◽  
Xiao Tang ◽  
Jiang Zhu ◽  
Zifa Wang ◽  
Jianjun Li ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Air Pollutants ◽  
Regional Scale ◽  
Monitoring Program ◽  
Health Impact ◽  
Horizontal Resolution ◽  
Training Data ◽  
Reanalysis Dataset ◽  
Surface Observations

Abstract. A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM2.5, PM10, SO2, NO2, CO, and O3) for the period 2013–2018 at high spatial (15 km×15 km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R2=0.52–0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4–39.3 µg/m3 for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R2=0.74–0.86 and RMSE =16.8–33.6 µg/m3 in different cities). Furthermore, through the comparison to satellite-estimated PM2.5 concentrations, we show that the accuracy of the PM2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users.

scholarly journals Assessment of the contribution of TEX air pollutants from Nigeria’s petroleum refineries to the ambient air quality: Part II

2021 ◽  
Vol 8 (1) ◽  
pp. 1947007
Author(s):  
Ebenezer Leke Odekanle ◽  
Chinchong Blessing Bakut ◽  
Abiodun Paul Olalekan ◽  
Roseline Oluwaseun Ogundokun ◽  
Charity O. Aremu ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Ambient Air ◽  
Ambient Air Quality ◽  
Petroleum Refineries

scholarly journals Health impact assessment of air pollutants during the COVID-19 pandemic in a Brazilian metropolis

2021 ◽  
Author(s):  
Marcos Lorran Paranhos Leão ◽  
Julia Oliveira Penteado ◽  
Sabrina Morales Ulguim ◽  
Rômulo Reginato Gabriel ◽  
Marina dos Santos ◽  
...  
Keyword(s):  
Impact Assessment ◽  
Air Pollutants ◽  
Health Impact Assessment ◽  
Health Impact

scholarly journals Spatial and Temporal Characteristics of Environmental Air Quality and Its Relationship with Seasonal Climatic Conditions in Eastern China during 2015–2018

2021 ◽  
Vol 18 (9) ◽  
pp. 4524
Author(s):  
Zhiyuan Wang ◽  
Xiaoyi Shi ◽  
Chunhua Pan ◽  
Sisi Wang
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Air Pollutants ◽  
Large Scale ◽  
Asian Summer Monsoon ◽  
Eastern China ◽  
Climatic Conditions ◽  
Uniform Structure ◽  
Concentration Limit ◽  
Environmental Air

Exploring the relationship between environmental air quality (EAQ) and climatic conditions on a large scale can help better understand the main distribution characteristics and the mechanisms of EAQ in China, which is significant for the implementation of policies of joint prevention and control of regional air pollution. In this study, we used the concentrations of six conventional air pollutants, i.e., carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone (O3), derived from about 1300 monitoring sites in eastern China (EC) from January 2015 to December 2018. Exploiting the grading concentration limit (GB3095-2012) of various pollutants in China, we also calculated the monthly average air quality index (AQI) in EC. The results show that, generally, the EAQ has improved in all seasons in EC from 2015 to 2018. In particular, the concentrations of conventional air pollutants, such as CO, SO2, and NO2, have been decreasing year by year. However, the concentrations of particulate matter, such as PM2.5 and PM10, have changed little, and the O3 concentration increased from 2015 to 2018. Empirical mode decomposition (EOF) was used to analyze the major patterns of AQI in EC. The first mode (EOF1) was characterized by a uniform structure in AQI over EC. These phenomena are due to the precipitation variability associated with the East Asian summer monsoon (EASM), referred to as the “summer–winter” pattern. The second EOF mode (EOF2) showed that the AQI over EC is a north–south dipole pattern, which is bound by the Qinling Mountains and Huaihe River (about 35° N). The EOF2 is mainly caused by seasonal variations of the mixed concentration of PM2.5 and O3. Associated with EOF2, the Mongolia–Siberian High influences the AQI variation over northern EC by dominating the low-level winds (10 m and 850 hPa) in autumn and winter, and precipitation affects the AQI variation over southern EC in spring and summer.

scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

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