scholarly journals Design of PM2.5 and PM10 measuring instruments for analysis of air pollution distribution patterns in the dramaga area based on internet of things

2021 ◽  
Vol 1816 (1) ◽  
pp. 012053
Author(s):  
H Satria ◽  
S Soekirno
2020 ◽  

Although current circumstances pose challenges to foretelling the future consequences of coronavirus spread, we consider environmental load-related researches became more and more important nowadays perhaps as never before. Many experts believe that the increasingly dire public health emergency situation, policy makers and word leaders should make it possible that the COVID-19 outbreak contributes to a transition of sustainable consumption. With the purpose of contributing to rethink the importance of sustainability efforts, here we present total suspended particulates (TSP) results which represent traffic emission caused air pollution in the three most populous cities of Ecuador obtained before, during, and after the: (i) the traffic measures entered into force on state level; (ii) curfew entered into force on state level; (iii) and quarantine entered into force (in Guayaquil, and whole Guayas province). We documented significant decrease in TSP emissions (PM2.5 and PM10) compared to normal traffic operation obtained from some four lanes roads in Quito, Guayaquil, and Cuenca. The most remarkable fall in suspended particulate values (96.47% decrease in PM2.5) compared to emission observed before traffic measures occurred in Cuenca.


Author(s):  
Chao Zhang ◽  
Zhenyu Quan ◽  
Qincheng Wu ◽  
Zhezhen Jin ◽  
Joseph Lee ◽  
...  

Background: Air pollution in large Chinese cities has led to recent studies that highlighted the relationship between particulate matters (PM) and elevated risk of cardio-cerebrovascular mortality. However, it is unclear as to whether: (1) The same adverse relations exist in cities with relatively low levels of air pollution; and (2) the relationship between the two are similar across ethnic groups. Methods: We collected data of PM2.5 (PM with an aerodynamic diameter ≤ 2.5 µm) and PM10 (aerodynamic diameter ≤ 10 µm) in the Yanbian Korean Autonomous Prefecture between 1 January 2015 and 31 December 2016. Using a time-stratified case-crossover design, we investigated whether levels of particulate pollutants influence the risk of cardio-cerebrovascular disease mortality among ethnic Korean vs. ethnic Han residents residing in the Yanbian Korean Autonomous Prefecture. Results: Under the single air pollutant model, the odds ratios (ORs) of cardio-cerebrovascular disease were 1.025 (1.024–1.026) for each 10 μg/m3 increase in PM2.5 at lag0 day, 1.012 (1.011–1.013) for each 10 μg/m3 increase in PM10 at lag1 day. In the multi-pollutant model adjusted by PM10, SO2, and NO2, the ORs of cardio-cerebrovascular disease were 1.150 (1.145–1.155) for ethnic Koreans and 1.154 (1.149–1.158) for ethnic Hans for each 10 μg/m3 increase in PM2.5. In the multi-pollutant model adjusted by PM2.5, SO2, and NO2, the ORs of cardio-cerebrovascular disease were 1.050 (1.047–1.053) for ethnic Koreans and 1.041 (1.039–1.043) for ethnic Hans for each 10 μg/m3 increase in PM10. Conclusion: This study showed that PM2.5 and PM10 were associated with increased risks of acute death events in residential cardio-cerebrovascular disease in Yanbian, China.


2016 ◽  
Vol 13 (4) ◽  
pp. 19-35 ◽  
Author(s):  
Lídice García Ríos ◽  
José Alberto Incera Diéguez

Sensor networks have perceived an extraordinary growth in the last few years. From niche industrial and military applications, they are currently deployed in a wide range of settings as sensors are becoming smaller, cheaper and easier to use. Sensor networks are a key player in the so-called Internet of Things, generating exponentially increasing amounts of data. Nonetheless, there are very few documented works that tackle the challenges related with the collection, manipulation and exploitation of the data generated by these networks. This paper presents a proposal for integrating Big Data tools (in rest and in motion) for gathering, storage and analysis of data generated by a sensor network that monitors air pollution levels in a city. The authors provide a proof of concept that combines Hadoop and Storm for data processing, storage and analysis, and Arduino-based kits for constructing their sensor prototypes.


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