scholarly journals Nghiên cứu ảnh hưởng của hiện tượng nghịch nhiệt đến hàm lượng bụi PM2.5 trong môi trường không khí tại Hà Nội

Author(s):  
Trinh Thi Tham

In this study, we assessed effects of temperature inversions on air quality in Hanoi, is the capital of Vietnam with the business development speed also as urbanization high in year near here. Temperature inversions occur frequently in the cooler seasons, exacerbating the impact of emissions and diffusions from industry and traffic. This research used concentration of PM2.5 data gathered from 02 automatic air quality monitoring station located North Centre for Environmental Monitoring, Vietnam environment administration and U.S Embassy Hanoi. The data on the change of temperature in the depth was collected from the meteorological stations Hanoi in 2017 aimed to analyze the frequency of the temperature  rating of the Heat Rate of the Heat Temperature and the Heat of the temperature  inversions and impacts of that on concentration of PM2.5 in the atmosphere. The results also revealed that there was statistical difference (Sig. <0,05) between PM2.5 levels in the ambient air on the inversion days and those on the normal day.

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 914 ◽  
Author(s):  
Yuhan Huang ◽  
John Zhou ◽  
Yang Yu ◽  
Wai-chuen Mok ◽  
Casey Lee ◽  
...  

Strict social distancing rules are being implemented to stop the spread of COVID-19 pandemic in many cities globally, causing a sudden and extreme change in the transport activities. This offers a unique opportunity to assess the effect of anthropogenic activities on air quality and provides a valuable reference to the policymakers in developing air quality control measures and projecting their effectiveness. In this study, we evaluated the effect of the COVID-19 lockdown on the roadside and ambient air quality in Hong Kong, China, by comparing the air quality monitoring data collected in January–April 2020 with those in 2017–2019. The results showed that the roadside and ambient NO2, PM10, PM2.5, CO and SO2 were generally reduced in 2020 when comparing with the historical data in 2017–2019, while O3 was increased. However, the reductions during COVID-19 period (i.e., February–April) were not always higher than that during pre-COVID-19 period (i.e., January). In addition, there were large seasonal variations in the monthly mean pollutant concentrations in every year. This study implies that one air pollution control measure may not generate obvious immediate improvements in the air quality monitoring data and its effectiveness should be evaluated carefully to eliminate the effect of seasonal variations.


2018 ◽  
Vol 57 ◽  
pp. 02013
Author(s):  
Bartosz Szulczyński ◽  
Jacek Gębicki

Described in this work are the results of field tests carried out in the Tricity Agglomeration between 01 April 2018 and 30 June 2018 in order to evaluate the usefulness of low-cost PM10 sensors in atmospheric air quality monitoring. The results were juxtaposed with the results obtained using reference methods. The results were validated based on the measurement uncertainty as described in the EU report "Demonstration of Equivalence of Ambient Air Monitoring Methods. EC Working Group on Guidance for the Demonstration of Equivalence". Moreover, the impact of external factors (humidity, pressure, temperature) on the obtained results was also assessed. It was shown that the low-cost sensors display measurement uncertainty which exceeds the acceptable values as compared to the reference methods and correction factors depending on the measured PM10 concentration need to be introduced in order to fulfil the criteria of equivalence.


Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.


2021 ◽  
pp. 94-106
Author(s):  
Porush Kumar ◽  
Kuldeep ◽  
Nilima Gautam

Air pollution is a severe issue of concern worldwide due to its most significant environmental risk to human health today. All substances that appear in excessive amounts in the environment, such as PM10, NO2, or SO2, may be associated with severe health problems. Anthropogenic sources of these pollutants are mainly responsible for the deterioration of urban air quality. These sources include stationary point sources, mobile sources, waste disposal landfills, open burning, and similar others. Due to these pollutants, people are at increased risk of various serious diseases like breathing problems and heart disease, and the death rate due to these diseases can also increase. Hence, air quality monitoring is essential in urban areas to control and regulate the emission of these pollutants to reduce the health impacts on human beings. Udaipur has been selected for the assessment of air quality with monitored air quality data. Air quality monitoring stations in Udaipur city are operated by the CPCB (Central Pollution Control Board) and RSPCB (Rajasthan State Pollution Control Board). The purpose of this study is to characterize the level of urban air pollution through the measurement of PM10, NO2, or SO2 in Udaipur city, Rajasthan (India). Four sampling locations were selected for Udaipur city to assess the effect of urban air pollution and ambient air quality, and it was monitored for a year from 1st January 2019 to 31st December 2019. The air quality index has been calculated with measured values of PM10, NO2, and SO2. The concentration of PM10 is at a critical level of pollution and primarily responsible for bad air quality and high air quality Index in Udaipur city.


Author(s):  
Zablon W. Shilenje ◽  
Kennedy Thiong’o ◽  
Kennedy I. Ondimu ◽  
Paul M. Nguru ◽  
John Kaniaru Nguyo ◽  
...  

2013 ◽  
Vol 380-384 ◽  
pp. 1077-1080
Author(s):  
Jin Gang Li ◽  
Xiao Hong Su ◽  
Hong Wei Xuan ◽  
Shi Lei Zhao

In order to enhance the organization and management efficiency of multi-source heterogeneous data in the collection process for urban ambient air quality monitoring, according to the analysis of the limitations, the existing methods and the features of data collected, a new kind of multi-sensor and multi-level information fusion approach based on vague sets is proposed. The approach takes full advantage of the redundancy and complementarities from inter-level information to achieve the purpose of information integration. The mathematical description of vague sets based on the multi-sensor information fusion is defined and the corresponding model is developed in which the data organization and the monitoring method and the implementation of the hierarchical algorithm are discussed. Finally, the proposed approach is applied to a computing system of the ambient air quality monitoring. The study of this approach can supply scientific accordance for comprehensive monitoring of urban ambient air quality.


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