scholarly journals Particulate matter and emergency visits for asthma: a time-series study of their association in the presence and absence of wildfire smoke in Reno, Nevada, 2013-2018

2020 ◽  
Author(s):  
Daniel Kiser ◽  
William J. Metcalf ◽  
Gai Elhanan ◽  
Brendan Schnieder ◽  
Karen Schlauch ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Urgent Care ◽  
Pm 2.5 ◽  
Wildfire Smoke ◽  
The Difference ◽  
Pm 10 ◽  
Washoe County

Abstract Background: Health risks due to particulate matter (PM) from wildfires may differ from risk due to PM from other sources. In places frequently subjected to wildfire smoke, such as Reno, Nevada, it is critical to determine whether wildfire PM poses unique risks. Our goal was to quantify the difference in the association of adverse asthma events with PM on days when wildfire smoke was present versus days when wildfire smoke was not present. Methods: We obtained counts of visits for asthma at emergency departments and urgent care centers from a large regional healthcare system in Reno for the years 2013-2018. We also obtained dates when wildfire smoke was present from the Washoe County Health District Air Quality Management Division. We then examined whether the presence of wildfire smoke modified the association of PM 2.5 , PM 10-2.5 , and PM 10 with asthma visits using generalized additive models. We improved on previous studies by accounting for possible non-linearity in the association between PM concentration and asthma visits: wildfire-smoke days where the PM concentration exceeded the maximum PM concentration on other days were excluded. Results: Air quality was affected by wildfire smoke on 188 days between 2013 and 2018. We found that the presence of wildfire smoke increased the association of a 5 µg/m 3 increase in daily and three-day averages of PM 2.5 with asthma visits by 6.1% (95% confidence interval (CI): 2.1-10.3%) and 6.8% (CI: 1.2-12.7%), respectively. Similarly, the presence of wildfire smoke increased the association of a 5 µg/m 3 increase in daily and three-day averages of PM 10 with asthma visits by 5.5% (CI: 2.5-8.6%) and 7.2% (CI: 2.6-12.0%), respectively. We did not observe any significant increases in association for PM 10-2.5 or for seven-day averages of PM 2.5­ and PM 10 . Conclusions: Since we found significantly stronger associations of PM 2.5 and PM 10 with asthma visits when wildfire smoke was present, our results suggest that wildfire PM is more hazardous than non-wildfire PM for patients with asthma.

scholarly journals Particulate matter and emergency visits for asthma: a time-series study of their association in the presence and absence of wildfire smoke in Reno, Nevada, 2013-2018

2020 ◽  
Author(s):  
Daniel Kiser ◽  
William J. Metcalf ◽  
Gai Elhanan ◽  
Brendan Schnieder ◽  
Karen Schlauch ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Urgent Care ◽  
Pm 2.5 ◽  
Wildfire Smoke ◽  
The Difference ◽  
Pm 10 ◽  
Washoe County

Abstract Background: Health risks due to particulate matter (PM) from wildfires may differ from risk due to PM from other sources. In places frequently subjected to wildfire smoke, such as Reno, Nevada, it is critical to determine whether wildfire PM poses unique risks. Our goal was to quantify the difference in the association of adverse asthma events with PM on days when wildfire smoke was present versus days when wildfire smoke was not present. Methods: We obtained counts of visits for asthma at emergency departments and urgent care centers from a large regional healthcare system in Reno for the years 2013-2018. We also obtained dates when wildfire smoke was present from the Washoe County Health District Air Quality Management Division. We then examined whether the presence of wildfire smoke modified the association of PM 2.5 , PM 10-2.5 , and PM 10 with asthma visits using generalized additive models. We improved on previous studies by accounting for possible non-linearity in the association between PM concentration and asthma visits: wildfire-smoke days where the PM concentration exceeded the maximum PM concentration on other days were excluded. Results: Air quality was affected by wildfire smoke on 188 days between 2013 and 2018. We found that the presence of wildfire smoke increased the association of a 5 µg/m 3 increase in daily and three-day averages of PM 2.5 with asthma visits by 6.1% (95% confidence interval (CI): 2.1-10.3%) and 6.8% (CI: 1.2-12.7%), respectively. Similarly, the presence of wildfire smoke increased the association of a 5 µg/m 3 increase in daily and three-day averages of PM 10 with asthma visits by 5.5% (CI: 2.5-8.6%) and 7.2% (CI: 2.6-12.0%), respectively. We did not observe any significant increases in association for PM 10-2.5 or for seven-day averages of PM 2.5­ and PM 10 . Conclusions: Since we found significantly stronger associations of PM 2.5 and PM 10 with asthma visits when wildfire smoke was present, our results suggest that wildfire PM is more hazardous than non-wildfire PM for patients with asthma.

scholarly journals Trend of Particulate Matter PM 2.5 and PM 10 in Dhaka City

1970 ◽  
Vol 46 (3) ◽  
pp. 389-398 ◽  
Author(s):  
MA Rouf ◽  
M Nasiruddin ◽  
AMS Hossain ◽  
MS Islam
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Winter Season ◽  
Ambient Air ◽  
Quality Data ◽  
Dhaka City ◽  
Monsoon Period ◽  
Pm 2.5 ◽  
Pm 10

Dhaka City has been affecting with severe air pollution particularly by particulate matter. The ambient air quality data for particulate matter were collected during April 2002 to September 2005 at the Continuous Air Quality Monitoring Station (CAMS) located at Sangshad Bhaban, Dhaka. Data reveal that the pollution from particulate matter greatly varies with climatic condition. While the level comes down the limit value in the monsoon period (April-October), it goes beyond the limit during non-monsoon time (November-March). The latest data show that during monsoon period PM 10 concentration varies from 50 μg/m3 to 80 μg/m3 and PM 2.5 concentration from 20 μg/m3 to 60 μg/m3 and during non monsoon period PM 10 varies from 100 μg/m3 to 250 μg/m3 and PM 2.5 varies from 70 μg/m3 to 165 μg/m3. The seasonal variation clearly indicates the severe PM 10 pollution during the dry winter season and also sometime during post-monsoon season in Dhaka City. Keywords: Air pollution; PM 2.5; PM 10; Air quality DOI: http://dx.doi.org/10.3329/bjsir.v46i3.9049 BJSIR 2011; 46(3): 389-398

scholarly journals Using Multi-Angle Imaging SpectroRadiometer Aerosol Mixture Properties for Air Quality Assessment in Mongolia

2018 ◽  
Vol 10 (8) ◽  
pp. 1317 ◽  
Author(s):  
Meredith Franklin ◽  
Khang Chau ◽  
Olga Kalashnikova ◽  
Michael Garay ◽  
Temuulen Enebish ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Capital City ◽  
World Health ◽  
Dust Particles ◽  
Support Vector ◽  
Daily Maximum ◽  
Pm 2.5 ◽  
Pm 10

Ulaanbaatar (UB), the capital city of Mongolia, has extremely poor wintertime air quality with fine particulate matter concentrations frequently exceeding 500 μg/m3, over 20 times the daily maximum guideline set by the World Health Organization. Intensive use of sulfur-rich coal for heating and cooking coupled with an atmospheric inversion amplified by the mid-continental Siberian anticyclone drive these high levels of air pollution. Ground-based air quality monitoring in Mongolia is sparse, making use of satellite observations of aerosol optical depth (AOD) instrumental for characterizing air pollution in the region. We harnessed data from the Multi-angle Imaging SpectroRadiometer (MISR) Version 23 (V23) aerosol product, which provides total column AOD and component-particle optical properties for 74 different aerosol mixtures at 4.4 km spatial resolution globally. To test the performance of the V23 product over Mongolia, we compared values of MISR AOD with spatially and temporally matched AOD from the Dalanzadgad AERONET site and find good agreement (correlation r = 0.845, and root-mean-square deviation RMSD = 0.071). Over UB, exploratory principal component analysis indicates that the 74 MISR AOD mixture profiles consisted primarily of small, spherical, non-absorbing aerosols in the wintertime, and contributions from medium and large dust particles in the summertime. Comparing several machine learning methods for relating the 74 MISR mixtures to ground-level pollutants, including particulate matter with aerodynamic diameters smaller than 2.5 μm ( PM 2.5 ) and 10 μm ( PM 10 ), as well as sulfur dioxide ( SO 2 ), a proxy for sulfate particles, we find that Support Vector Machine regression consistently has the highest predictive performance with median test R 2 for PM 2.5 , PM 10 , and SO 2 equal to 0.461, 0.063, and 0.508, respectively. These results indicate that the high-dimensional MISR AOD mixture set can provide reliable predictions of air pollution and can distinguish dominant particle types in the UB region.

scholarly journals A comparative assessment of air pollutants of smog in wagah border and other sites in Lahore, Pakistan

2024 ◽  
Vol 84 ◽  
Author(s):  
H. S. Yousaf ◽  
M. Abbas ◽  
N. Ghani ◽  
H. Chaudhary ◽  
A. Fatima ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Air Pollutants ◽  
Quality Index ◽  
Pearson Correlation ◽  
Air Quality Index ◽  
Pm 2.5 ◽  
Secondary Air Pollutants ◽  
Secondary Air ◽  
Pm 10

Abstract Smog has become the fifth season of Pakistan especially in Lahore city. Increased level of air pollutants (primary and secondary) are thought to be responsible for the formation of smog in Lahore. Therefore, the current study was carried out for the evaluation of air pollutants (primary and secondary) of smog in Wagah border particularly and other sites (Jail road, Gulburg) Lahore. For this purpose, baseline data on winter smog from March to December on primary and secondary air pollutants and meteorological parameters was collected from Environmental Protection Department and Pakistan Meteorological Department respectively. Devices being used in both departments for analysis of parameters were also studied. Collected data was further statistically analyzed to determine the correlation of parameters with meteorological conditions and was subjected to air quality index. According to results, PM 10 and PM 2.5 were found very high above the NEQS. NOx concentrations were also high above the permissible limits whereas SO2 and O3 were found below the NEQS thus have no roles in smog formation. Air Quality Index (AQI) of pollutants was PM 2.5(86-227), PM 10 (46-332), NOx (26-110), O3 (19-84) and SO2 (10-95). AQI of PM 2.5 remained between moderate to very unhealthy levels. AQI of PM 10 remained between good to hazardous levels. AQI of NOx remained between good to unhealthy for sensitive groups’ levels. AQI of O3 and SO2 remained between good to moderate levels. Pearson correlation showed that every pollutant has a different relation with different or same parameters in different areas. It is concluded from the present study that particulate matter was much more responsible for smog formation. Although NOx also played role in smog formation. So there is need to reduce sources of particulate matter and NOx specifically in order to reduce smog formation in Lahore.

scholarly journals Antibiotic resistance associated with air quality and transferred by airborne particulate matter

2020 ◽  
Author(s):  
Zhen-Chao Zhou ◽  
Yang Liu ◽  
Ze-Jun Lin ◽  
Xin-Yi Shuai ◽  
Lin Zhu ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Particulate Matter ◽  
Air Quality ◽  
Diversity Index ◽  
Public Health Problem ◽  
Airborne Particulate Matter ◽  
Cell Membrane Permeability ◽  
Airborne Particulate ◽  
Pm 2.5 ◽  
Pm 10

Abstract Background: Environmental spread of antibiotic resistance has become a public health problem, but the relationship between air quality and antibiotic resistance has not been fully investigated, the effect and mechanism of air pollutants on horizontal genes transfer of antibiotic resistance are rarely mentioned. Results: In the present study, significant correlation ( P < 0.05) between resistance rates of 10 clinical antibiotic-resistant pathogens and number of days when air quality index was below 100 were observed in an extensive survey from 2014 to 2017 in China. In addition, characterization of antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and bacterial communities in airborne particulate matter (PM), dust, and human airway sputum samples were profiled in a hospital. PM 2.5 and PM 10 were shown to contain ARGs and MGEs high relative abundance and diversity index. Importantly, transferable multi-resistant plasmids were identified from air, e.g., pTAir-3 with 26 MGEs and 10 ARGs, using conjugative mating assays and nanopore sequencing . Furthermore, PM 2.5 and PM 10 significantly enhanced the conjugative transfer frequencies between bacteria, via inducing increased levels of reactive oxygen species and cell membrane permeability; upregulated gene expression levels were confirmed by genome-wide RNA sequencing. Conclusions: These findings provide a new perspective on antibiotic resistance research and have profound implications for antibiotic resistance control in clinics and environments.

Exercise Performed Concomitantly with Particulate Matter Exposure Inhibits Lung Injury

2017 ◽  
Vol 39 (02) ◽  
pp. 133-140 ◽  
Author(s):  
Adriano Silva-Renno ◽  
Guilherme Baldivia ◽  
Manoel Oliveira-Junior ◽  
Maysa Brandao-Rangel ◽  
Elias El-Mafarjeh ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Systemic Inflammation ◽  
Air Pollutants ◽  
Lung Parenchyma ◽  
Treadmill Training ◽  
Tnf Alpha ◽  
Systemic Response ◽  
Pm 2.5 ◽  
Pm 10 ◽  
Particulate Matter Exposure

AbstractAir pollution is a growing problem worldwide, inducing and exacerbating several diseases. Among the several components of air pollutants, particulate matter (PM), especially thick (10–2.5 µm; PM 10) and thin (≤2.5 µm; PM 2.5), are breathable particles that easily can be deposited within the lungs, resulting in pulmonary and systemic inflammation. Although physical activity is strongly recommended, its effects when practiced in polluted environments are questionable. Therefore, the present study evaluated the pulmonary and systemic response of concomitant treadmill training with PM 2.5 and PM 10 exposure. Treadmill training inhibited PM 2.5- and PM 10-induced accumulation of total leukocytes (p<0.001), neutrophils (p<0.001), macrophages (p<0.001) and lymphocytes (p<0.001) in bronchoalveolar lavage (BAL), as well as the BAL levels of IL-1beta (p<0.001), CXCL1/KC (p<0.001) and TNF-alpha (p<0.001), whereas it increased IL-10 levels (p<0.05). Similar effects were observed on accumulation of polymorphonuclear (p<0.01) and mononuclear (p<0.01) cells in the lung parenchyma and in the peribronchial space. Treadmill training also inhibited PM 2.5- and PM 10-induced systemic inflammation, as observed in the number of total leukocytes (p<0.001) and in the plasma levels of IL-1beta (p<0.001), CXCL1/KC (p<0.001) and TNF-alpha (p<0.001), whereas it increased IL-10 levels (p<0.001). Treadmill training inhibits lung and systemic inflammation induced by particulate matter.

scholarly journals Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research

2021 ◽  
Vol 13 (15) ◽  
pp. 2981
Author(s):  
Jeanné le Roux ◽  
Sundar Christopher ◽  
Manil Maskey
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Land Cover ◽  
Spectral Response ◽  
Fine Particulate Matter ◽  
Daily Basis ◽  
Land Cover Types ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
Geolocation Accuracy

Planet, a commercial company, has achieved a key milestone by launching a large fleet of small satellites (smallsats) that provide high spatial resolution imagery of the entire Earth’s surface on a daily basis with its PlanetScope sensors. Given the potential utility of these data, this study explores the use for fine particulate matter (PM2.5) air quality applications. However, before these data can be utilized for air quality applications, key features of the data, including geolocation accuracy, calibration quality, and consistency in spectral signatures, need to be addressed. In this study, selected Dove-Classic PlanetScope data is screened for geolocation consistency. The spectral response of the Dove-Classic PlanetScope data is then compared to Moderate Resolution Imaging Spectroradiometer (MODIS) data over different land cover types, and under varying PM2.5 and mid visible aerosol optical depth (AOD) conditions. The data selected for this study was found to fall within Planet’s reported geolocation accuracy of 10 m (between 3–4 pixels). In a comparison of top of atmosphere (TOA) reflectance over a sample of different land cover types, the difference in reflectance between PlanetScope and MODIS ranged from near-zero (0.0014) to 0.117, with a mean difference in reflectance of 0.046 ± 0.031 across all bands. The reflectance values from PlanetScope were higher than MODIS 78% of the time, although no significant relationship was found between surface PM2.5 or AOD and TOA reflectance for the cases that were studied. The results indicate that commercial satellite data have the potential to address Earth-environmental issues.

scholarly journals Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

2020 ◽  
Author(s):  
Eric S. Coker ◽  
Ssematimba Joel ◽  
Engineer Bainomugisha
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Additive Models ◽  
Pollution Monitoring ◽  
Sub Saharan Africa ◽  
Land Use Regression ◽  
Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 &micro;g/m3 (IQR: 45.4-73.0 &micro;g/m3; median= 57.5 &micro;g/m3). For the ML LUR models, RMSE values ranged between 5.43 &micro;g/m3 - 15.43 &micro;g/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 &micro;g/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

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