pollutant concentrations
Recently Published Documents


TOTAL DOCUMENTS

810
(FIVE YEARS 291)

H-INDEX

46
(FIVE YEARS 7)

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 543
Author(s):  
Luigi Russi ◽  
Paolo Guidorzi ◽  
Beatrice Pulvirenti ◽  
Davide Aguiari ◽  
Giovanni Pau ◽  
...  

This work is aimed at the experimental characterisation of air quality and thermal profile within an electric vehicle cabin, measuring at the same time the HVAC system energy consumption. Pollutant concentrations in the vehicle cabin are measured by means of a low-cost system of sensors. The effects of the HVAC system configuration, such as fresh-air and recirculation mode, on cabin air quality, are discussed. It is shown that the PM concentrations observed in recirculation mode are lower than those in fresh-air mode, while VOC concentrations are generally higher in recirculation than in fresh-air mode. The energy consumption is compared in different configurations of the HVAC system. The novelty of this work is the combined measurement of important comfort parameters such as air temperature distribution and air quality within the vehicle, together with the real time energy consumption of the HVAC system. A wider concept of comfort is enabled, based on the use of low-cost sensors in the automotive field.


2022 ◽  
Author(s):  
Horim Kim ◽  
Michael Müller ◽  
Stephan Henne ◽  
Christoph Hüglin

Abstract. Low-cost sensors are considered as exhibiting great potential to complement classical air quality measurements in existing monitoring networks. However, the use of low-cost sensors poses some challenges. In this study, the behavior and performance of electrochemical sensors for NO and NO2 were determined over a longer operating period in a real-world deployment. After careful calibration of the sensors, based on co-location with reference instruments at a rural traffic site during six months and by using robust linear regression and random forest regression, the coefficient of determination of both types of sensors were high (R2 > 0.9) and the root mean square error (RMSE) of NO and NO2 sensors were about 6.8 ppb and 3.5 ppb, respectively, for 10-minute mean concentrations. The RMSE of the NO2 sensors, however, more than doubled, when the sensors were deployed without re-calibration for a one-year period at other site types (including urban background locations), where the range and the variability of air pollutant concentrations differed from the calibration site. This indicates a significant effect of the re-location of the sensors on the quality of their data. During deployment, we found that the NO2 sensors are capable of distinguishing general pollution levels, but they proved unsuitable for accurate measurements, mainly due to significant biases. In order to investigate the long-term stability of the original calibration, the sensors were re-installed at the calibration site after deployment. Surprisingly, the coefficient of determination and the RMSE of the NO sensor remained almost unchanged after more than one year of operation. In contrast, the performance of the NO2 sensors clearly deteriorated as indicated by a higher RMSE (about 7.5 ppb, 10-minute mean concentrations) and a lower coefficient of determination (R2 = 0.59).


2022 ◽  
Vol 40 ◽  
Author(s):  
Catiane Zanin Cabral ◽  
Alan da Silveira Fleck ◽  
Fernanda Chaves Amantéa ◽  
Claudia Ramos Rhoden ◽  
Sérgio Luis Amantéa

Abstract Objective: To evaluate air quality in the waiting room of a pediatric emergency service considering the serial concentrations of particulate matter (PM2.5), and to determine if the number of people present in the room can have an influence on the pollutant concentrations. Methods: Cross-sectional study, carried out in the waiting room of a reference pediatric hospital in the city of Porto Alegre, conducted in a one-year period, in a continuous-time sample including all of the four seasons of the year. The monitoring of PM2.5 was performed using a real-time aerosol monitor (DustTrak II). The number of people in the room was determined every hour and the climatic characteristics per daily mean. The concentration of PM2.5 and the number of people were expressed by mean and standard deviation. The means were compared by Analysis of Variance and Pearson's correlation coefficient. Results: There was a significant increase in the concentration of PM2.5 in the autumn, when compared to other seasons (p<0.001). The pollutant increase, in this season, was accompanied by the higher number of people in the emergency room (p=0.026). The association between PM2.5 and the number of people is confirmed by the positive correlation between these two variables (r=0.738; p<0.001). Conclusions: The pediatric emergency waiting room showed elevated PM2.5 in all seasons. The number of people in the room had a positive correlation with the concentration of the pollutant in the environment.


2022 ◽  
Vol 2159 (1) ◽  
pp. 012003
Author(s):  
L Rodríguez-Garavito ◽  
K J Romero-Corredor ◽  
C A Zafra-Mejía

Abstract This paper shows a multitemporal analysis with autoregressive integrated moving average models of the influence of atmospheric condition on concentrations of particulate matter ≤ 10 µm in Bogotá city, Colombia. Information was collected from six monitoring stations distributed throughout the city. The study period was nine years. Autoregressive component of the models suggests that urban areas with greater atmospheric instability show a lower hourly persistence of particulate matter (one hour) compared to urban areas with lower atmospheric instability (two hours). Moving average component of the models hints those urban areas with greater atmospheric instability show greater hourly variability in particulate matter concentrations (5-10 hours). The models also suggest that a high degree of air pollution decreases the temporal influence of the atmospheric condition on particulate matter concentrations; in this case, the temporal behavior of particulate matter possibly depends on the urban emission sources of this pollutant rather than on the existing atmospheric condition. This study is relevant to deepen the knowledge in relation to the following aspects of atmospheric physics: The use of statistical models for the time series analysis of atmospheric condition, and the analysis by statistical models of the influence of atmospheric condition on air pollutant concentrations.


2021 ◽  
Vol 22 (2) ◽  
pp. 85-94
Author(s):  
Rini Mariana Sibarani ◽  
Halda Aditya Belgaman ◽  
Ibnu Athoillah ◽  
Samba Wirahma

Intisari Selama ini Jakarta dikenal dengan kota berpolusi dengan indeks pencemaran udara yang cukup tinggi. Salah satu penyebab pencemaran udara adalah polutan partikulat (PM2.5) dan gas CO yang berasal dari pembakaran tidak sempurna. Pada masa pandemi Covid-19, pemerintah menerapkan  kebijakan Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) untuk menekan jumlah penularan virus Covid-19. Jakarta Pusat sebagai salah satu kota yang melaksanakan kebijakan PPKM mewajibkan perkantoran yang ada untuk menerapkan Work From Home (WFH). Jumlah konsentrasi polusi udara PM2.5 pada periode WFH Maret 2020 tidak jauh berbeda dibandingkan lima tahun sebelumnya. Sedangkan konsentrasi CO pada periode yang sama mengalami penurunan dibandingkan lima tahun sebelumnya. Hasil analisis hubungan antara pengaruh parameter cuaca dengan konsentrasi polutan menunjukkan pengaruh parameter cuaca kurang signifikan terhadap konsentrasi PM2.5 dan gas CO. Nilai R-Square adj antara beberapa parameter cuaca terhadap konsentrasi PM2.5 dan konsentrasi CO cukup kecil. Hal ini menunjukkan bahwa pengurangan nilai konsentrasi PM2.5 dan gas CO bukan dipengaruhi oleh parameter cuaca. Faktor lain yang diduga memengaruhi konsentrasi PM2.5 dan gas CO adalah kegiatan manusia seperti WFH sehingga terjadi pengurangan aktivitas masyarakat untuk pergi ke kantor. Abstract Jakarta is known as a polluted city with a high air pollution index. One of the air pollution causes is particulate pollutants (PM2.5) and CO from incomplete combustion. During the Covid-19 pandemic, the government implemented a policy of Community Activity Restrictions, known as PPKM, to reduce the number of transmissions of the Covid-19. As one of the cities implementing the PPKM policy, Central Jakarta requires offices to implement Work From Home (WFH). The total PM2.5 air pollution concentration in the March 2020 WFH period was not much different from the previous five years. However, the CO concentration in the same period decreased compared to the last five years. Relationship analysis between the influence of weather parameters and pollutant concentrations shows that weather parameters are less significant on PM2.5 and CO concentration. The R-Square adj between several weather parameters on the concentration of PM2.5 and the concentration of CO is small. It means that weather parameters do not influence the reduction in the concentration of PM2.5 and CO. It is assumed that the PM2.5 and CO concentrations decreased due to fewer human activities in the office and public areas.


Abstract The Boundary-layer Air Quality-analysis Using Network of Instruments (BAQUNIN) supersite is presented. The site has been collecting pollutant concentrations and meteorological parameters since 2017. Currently, BAQUNIN consists of three observation sites located in the city center of Rome (Italy), and in the neighboring semi-rural and rural areas. To the best of our knowledge, BAQUNIN is one of the first observatories in the world to involve several passive and active ground-based instruments installed in multiple locations, managed by different research institutions, in a highly polluted megacity affected by coastal weather regimes. BAQUNIN has been promoted by the European Space Agency to establish an experimental research infrastructure for the validation of present and future satellite atmospheric products and the in-depth investigation of the planetary and urban boundary layers. Here, the main characteristics of the three sites are described, providing information about the complex instrumental suite and the produced data. The supersite adopts a policy of free sharing of its validated dataset with the community. Finally, the BAQUNIN potential is demonstrated with a case study involving a major fire that occurred in a waste treatment plant near the urban center of Rome, and the consequent investigation of the plume properties revealed by different instruments.


Author(s):  
Bolor-Erdene Turmunkh ◽  

Air pollution of the countries of Central Asia has affected not only the health of the population since 1990 but also influenced the environment. This study has been made empirically analyzes the spatial autocorrelation analysis that is based on the 1991 to 2017 database of Central Asian countries on the socio-economic factors influencing the concentration of Sulfur Dioxide (SO2), Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Ozone (O3), and Particulate Matter (PM2.5) in the air. Besides, this study validated Global Moran's I statistics to determine spatial positive autocorrelations. The results show that there is a strong correlation between air pollution concentrations and Gross Domestic Product (GDP) per capita. The achievement identified that the concentrations of SO2, CO, NO2, O3, and PM2.5 have a spatial aggregation and distribution effect, which is significantly influenced by the spatial characteristics and the Central Asian Regional Economic Unions. It also determined that an energy policy of a country can be affected the emissions of air pollutants from neighboring countries due to policy effects. Therefore, there is a need for regional coordination of environmental policies and the transfer of pollution-intensive industries, to keep air pollution in countries of Central Asia at a normal level. In addition to the empirical results of this study, the following two conclusions can be identified. First, it identified the need for a unified policy to reduce air pollution to reduce emissions from air pollution sources. Second, there is a need for a renewable energy policy for the development and promotion of renewable energy.


Environments ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 137
Author(s):  
Peter Brimblecombe ◽  
Meng-Yuan Chu ◽  
Chun-Ho Liu ◽  
Zhi Ning

Busy street canyons can have a large flow of vehicles and reduced air exchange and wind speeds at street level, exposing pedestrians to high pollutant concentrations. The airflow tended to move with vehicles along the canyon and the 1-s concentrations of NO, NO2 and CO were highly skewed close to the road and more normally distributed at sensors some metres above the road. The pollutants were more autocorrelated at these elevated sensors, suggesting a less variable concentration away from traffic in the areas of low turbulence. The kerbside concentrations also showed cyclic changes approximating nearby traffic signal timing. The cross-correlation between the concentration measurements suggested that the variation moved at vehicle speed along the canyon, but slower vertically. The concentrations of NOx and CO were slightly higher at wind speeds of under a metre per second. The local ozone concentrations had little effect on the proportion of NOx present as NO2. Pedestrians on the roadside would be unlikely to exceed the USEPA hourly guideline value for NO2 of 100 ppb. Across the campaign period, 100 individual minutes exceeded the guidelines, though the effect of short-term, high-concentration exposures is not well understood. Tram stops at the carriageway divider are places where longer exposures to higher levels of traffic-associated pollutants are possible.


F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1279
Author(s):  
Naveen Palanichamy ◽  
Su-Cheng Haw ◽  
Subramanian S ◽  
Kuhaneswaran Govindasamy ◽  
Rishanti Murugan

Particulate matter (PM), an air pollutant that is detrimental to breathing, is either emitted or formed ambiently. The exposure of respiratory system towards PM2.5, the fine particles of 2.5 micrometres diameter, causes complication for health. Thus, developing pollution control strategies requires the prediction of PM2.5 concentrations. Advancement of technology and computer science knowledge, machine learning (ML) algorithms are used for highly accurate prediction of air pollutant concentrations. Recently, air quality in Smart Cities of Malaysia has been getting worse due to industrialization, emissions from private motor vehicles, and transboundary haze pollution. Therefore, the forecasting of PM2.5 emissions to ensure they are within the statutory limits becomes necessary. Several machine learning methods have been implemented in existing research to predict air pollution concentrations in comparison to PM2.5. However, very few studies have used ML techniques to predict air quality in Malaysia when compared with global studies. Hence, to create awareness on the ML techniques and promote further research in this area, this study reviews and highlights most of the existing ML techniques for the prediction of PM2.5.


Sign in / Sign up

Export Citation Format

Share Document