scholarly journals SPATIAL TEMPORAL MODELLING OF PARTICULATE MATTER FOR HEALTH EFFECTS STUDIES

2016 ◽  
Vol XLI-B8 ◽  
pp. 1403-1406 ◽  
Author(s):  
N. A. S. Hamm
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Health Effects ◽  
Missing Values ◽  
Prediction Interval ◽  
Low Cost ◽  
Quality Measurement ◽  
Health Impact ◽  
Epidemiological Studies ◽  
Space Time

Epidemiological studies of the health effects of air pollution require estimation of individual exposure. It is not possible to obtain measurements at all relevant locations so it is necessary to predict at these space-time locations, either on the basis of dispersion from emission sources or by interpolating observations. This study used data obtained from a low-cost sensor network of 32 air quality monitoring stations in the Dutch city of Eindhoven, which make up the ILM (innovative air (quality) measurement system). These stations currently provide PM10 and PM2.5 (particulate matter less than 10 and 2.5 m in diameter), aggregated to hourly means. The data provide an unprecedented level of spatial and temporal detail for a city of this size. Despite these benefits the time series of measurements is characterized by missing values and noisy values. In this paper a space-time analysis is presented that is based on a dynamic model for the temporal component and a Gaussian process geostatistical for the spatial component. Spatial-temporal variability was dominated by the temporal component, although the spatial variability was also substantial. The model delivered accurate predictions for both isolated missing values and 24-hour periods of missing values (RMSE = 1.4 <i>&amp;mu;</i>g m<sup>&amp;minus;3</sup> and 1.8 <i>&amp;mu;</i>g m<sup>&amp;minus;3</sup> respectively). Outliers could be detected by comparison to the 95% prediction interval. The model shows promise for predicting missing values, outlier detection and for mapping to support health impact studies.

scholarly journals Particulate Air Pollution and Its Impact on Health in Vilnius and Kaunas

Medicina ◽  
2012 ◽  
Vol 48 (9) ◽  
pp. 70
Author(s):  
Hans Orru ◽  
Aida Laukaitienė ◽  
Ingrida Zurlytė
Keyword(s):  
Particulate Matter ◽  
Health Effects ◽  
Risk Estimation ◽  
Inorganic Compounds ◽  
Health Impact ◽  
Epidemiological Studies ◽  
Average Concentration ◽  
Years Of Life Lost ◽  
Contributing Factors ◽  
Short Term Exposure

Particulate matter in outdoor air has a significant impact on health. Small particles, composed of a variety of organic and inorganic compounds, are inhaled deep into the respiratory tract. The mechanisms and outcomes are manifold, resulting mainly in cardiopulmonary diseases. The current study aimed to quantify the health effects of particulate pollutants in Vilnius and Kaunas. Material and Methods. For risk estimation, the methodology of health impact assessment was employed. The exposure was defined as annual PM2.5 levels for long-term exposure effects and daily PM10 averages for short-term exposure effects. The baseline mortality/morbidity data were retrieved from health registers and exposure-response relationships from previous epidemiological studies. For health impact calculations, the WHO-developed tool AirQ was also applied. Results. The annual average concentration of PM2.5 was 11 μg/m3 in Vilnius and 17.5 μg/m3 in Kaunas. The exposure above the natural background corresponded annually to 263 (95% CI, 68– 464) and 338 (95% CI, 86–605) premature deaths in Vilnius and Kaunas. This resulted in 3438 (95% CI, 905–5952) and 3693 (95% CI, 983–6322) years of life lost and in an average decrease in life expectancy of 0.43 (95% CI, 0.11–0.74) and 0.69 (95% CI, 0.18–1.19) years, respectively. In addition, 143 (95% CI, 86–200) and 129 (95% CI, 78–179) respiratory and 297 (95% CI, 188–377) and 267 (95% CI, 169–338) cardiovascular hospitalizations per year could be expected in Vilnius and Kaunas, respectively. Conclusions. There is substantial exposure to particulate matter in the main Lithuanian cities, which causes considerable adverse health effects. Traffic and domestic heating are considered locally the most important contributing factors to the degradation of air quality.

scholarly journals A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6819
Author(s):  
Brigida Alfano ◽  
Luigi Barretta ◽  
Antonio Del Giudice ◽  
Saverio De Vito ◽  
Girolamo Di Francia ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Health Effects ◽  
Low Cost ◽  
Reference Method ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Extensive Review ◽  
Research Groups ◽  
International Regulations

The concerns related to particulate matter’s health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device’s operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R2 values lower than 0.5 are observed.

scholarly journals Exposure to Atmospheric Particulate Matter-Bound Polycyclic Aromatic Hydrocarbons and Their Health Effects: A Review

2021 ◽  
Vol 18 (4) ◽  
pp. 2177
Author(s):  
Lu Yang ◽  
Hao Zhang ◽  
Xuan Zhang ◽  
Wanli Xing ◽  
Yan Wang ◽  
...  
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Particulate Matter ◽  
Real Time ◽  
Health Effects ◽  
Aromatic Hydrocarbons ◽  
Anthropogenic Activities ◽  
Personal Exposure ◽  
Epidemiological Studies ◽  
Atmospheric Particulate Matter ◽  
Polycyclic Aromatic

Particulate matter (PM) is a major factor contributing to air quality deterioration that enters the atmosphere as a consequence of various natural and anthropogenic activities. In PM, polycyclic aromatic hydrocarbons (PAHs) represent a class of organic chemicals with at least two aromatic rings that are mainly directly emitted via the incomplete combustion of various organic materials. Numerous toxicological and epidemiological studies have proven adverse links between exposure to particulate matter-bound (PM-bound) PAHs and human health due to their carcinogenicity and mutagenicity. Among human exposure routes, inhalation is the main pathway regarding PM-bound PAHs in the atmosphere. Moreover, the concentrations of PM-bound PAHs differ among people, microenvironments and areas. Hence, understanding the behaviour of PM-bound PAHs in the atmosphere is crucial. However, because current techniques hardly monitor PAHs in real-time, timely feedback on PAHs including the characteristics of their concentration and composition, is not obtained via real-time analysis methods. Therefore, in this review, we summarize personal exposure, and indoor and outdoor PM-bound PAH concentrations for different participants, spaces, and cities worldwide in recent years. The main aims are to clarify the characteristics of PM-bound PAHs under different exposure conditions, in addition to the health effects and assessment methods of PAHs.

scholarly journals Long-term Particulate Matter Modeling for Health Effects Studies in California – Part II: Concentrations and Sources of Ultrafine Organic Aerosols

2016 ◽  
Author(s):  
Jianlin Hu ◽  
Shantanu Jathar ◽  
Hongliang Zhang ◽  
Qi Ying ◽  
Shu-Hua Chen ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Health Effects ◽  
Waste Disposal ◽  
Size Fraction ◽  
Organic Aerosols ◽  
Epidemiological Studies ◽  
Anthropogenic Sources ◽  
Major Constituent ◽  
Spatial Distributions ◽  
Mobile Emissions

Abstract. Organic aerosol (OA) is a major constituent of ultrafine particulate matter (PM0.1). Recent epidemiological studies have identified associations between PM0.1 OA and premature mortality and low birth weight. In this study, the source-oriented UCD/CIT model was used to simulate the concentrations and sources of primary organic aerosols (POA) and secondary organic aerosols (SOA) in PM0.1 in California for a 9-year (2000–2008) modeling period with 4 km horizontal resolution to provide more insights about PM0.1 OA for health effects studies. As a related quality control, predicted monthly average concentrations of fine particulate matter (PM2.5) total organic carbon at six major urban sites had mean fractional bias of −0.31 to 0.19 and mean fractional errors of 0.4 to 0.59. The predicted ratio of PM2.5 SOA/OA was lower than estimates derived from chemical mass balance (CMB) calculations by a factor of 2 ~ 3, which suggests the potential effects of processes such as POA volatility, additional SOA formation mechanism, and missing sources. OA in PM0.1, the focus size fraction of this study, is dominated by POA. Wood smoke is found to be the single biggest source of PM0.1 OA in winter in California, while meat cooking, mobile emissions (gasoline and diesel engines), and other anthropogenic sources (mainly solvent usage and waste disposal) are the most important sources in summer. Biogenic emissions are predicted to be the largest PM0.1 SOA source, followed by mobile sources and other anthropogenic sources, but these rankings are sensitive to the SOA model used in the calculation. Air pollution control programs aiming to reduce the PM0.1 OA concentrations should consider controlling solvent usage, waste disposal, and mobile emissions in California, but these findings should be revisited after the latest science is incorporated into the SOA exposure calculations. The spatial distributions of SOA associated with different sources are not sensitive to the choice of SOA model, although the absolute amount of SOA can change significantly. Therefore, the spatial distributions of PM0.1 POA and SOA over the 9-year study period provide useful information for epidemiological studies to further investigate the associations with health outcomes.

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.

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.

Solution to the Problem of Calibration of Low-Cost Air Quality Measurement Sensors in Networks

ACS Sensors ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 832-843 ◽  
Author(s):  
Georgia Miskell ◽  
Jennifer A. Salmond ◽  
David E. Williams
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Quality Measurement

scholarly journals Medellin Air Quality Initiative (MAUI)

2021 ◽  
Author(s):  
Andres Yarce Botero ◽  
Olga Lucia Quintero Montoya ◽  
Santiago Lopez-Restrepo ◽  
Nicolás Pinel ◽  
Jhon Edinson Hinestroza ◽  
...  
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Human Health ◽  
Long Range ◽  
Low Cost ◽  
Region Of Interest ◽  
Pollution Assessment ◽  
Health Impact ◽  
Cellular Damage

This chapter book presents Medellín Air qUality Initiative or MAUI Project; it tells a brief story of this teamwork, their scientific and technological directions. The modeling work focuses on the ecosystems and human health impact due to the exposition of several pollutants transported from long-range places and deposited. For this objective, the WRF and LOTOS-EUROS were configurated and implemented over the región of interest previously updating some input conditions like land use and orography. By other side, a spinoff initiative named SimpleSpace was also born during this time, developing, through this instrumentation branch a very compact and modular low-cost sensor to deploy in new air quality networks over the study domain. For testing this instrument and find an alternative way to measure pollutants in the vertical layers, the Helicopter In-Situ Pollution Assessment Experiment HIPAE misión was developed to take data through the overflight of a helicopter over Medellín. From the data obtained from the Simple units and other experiments in the payload, a citogenotoxicity analysis quantify the cellular damage caused by the exposition of the pollutants.

Sign in / Sign up

Export Citation Format

Share Document