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Context 1
... Thailand, Cambodia, Singapore, Taiwan, Hong Kong, Macau, Qatar and the United States (17 cities: Bangkok, Ayutthaya, Phetchaburi, Hua Hin, Pattaya, Sattahip, Aranyaprathet, Poipet, Siem Reap, Phnom Penh, Singapore, Taipei, Hong Kong, Macau, Doha, Chicago and Madison) were visited in 37 days. A summarized Southeast Asia travel plan is shown in Fig. 1. There was no pre-designated travel plan or time distribution in the countries in order to mimic the unpredictable travel choices of a solo traveler. 18 days were spent in Thailand, 6 days in Cambodia, 3 days each in Singapore and Taiwan, 6 days in Hong Kong (nearly half of the day in Macau) and the rest of the activity data was ...
Context 2
... Thailand, Cambodia, Singapore, Taiwan, Hong Kong, Macau, Qatar and the United States (17 cities: Bangkok, Ayutthaya, Phetchaburi, Hua Hin, Pattaya, Sattahip, Aranyaprathet, Poipet, Siem Reap, Phnom Penh, Singapore, Taipei, Hong Kong, Macau, Doha, Chicago and Madison) were visited in 37 days. A summarized Southeast Asia travel plan is shown in Fig. 1. There was no pre-designated travel plan or time distribution in the countries in order to mimic the unpredictable travel choices of a solo traveler. 18 days were spent in Thailand, 6 days in Cambodia, 3 days each in Singapore and Taiwan, 6 days in Hong Kong (nearly half of the day in Macau) and the rest of the activity data was ...
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Citations
... 45 The major advantage of these monitors, in addition to providing highly accurate data, are their low-cost, lowmaintenance and low-energy requirements (they could run 48-68 hours continuously on lithium batteries (22 000-28 000 mAh)). Although the monitors have been used in other studies in Southeast Asian countries 46 and India, 42 47 we pilot tested them under the local conditions of the study areas in Bangladesh prior to conducting the full CSPCS. All nine Duke monitors were run simultaneously for 24 hours in the same location on 10 different days. ...
Purpose
The Cook Stove Pregnancy Cohort Study (CSPCS) was designed to assess the effects of biomass fuel use on household air pollution (HAP) as well as the effects of HAP (fine particulate matter, PM 2.5 ) on birth outcomes and acute lower respiratory infection (ALRI) among infants in Bangladesh.
Participants
We recruited 903 women within 18 weeks of pregnancy from rural and semiurban areas of Bangladesh between November 2016 and March 2017. All women and their infants (N=831 pairs) were followed until 12 months after delivery and a subset have undergone respiratory and gut microbiota analysis.
Methods
Questionnaires were administered to collect detailed sociodemographic, medical, nutritional and behavioural information on the mother–child dyads. Anthropometric measurements and biological samples were also collected, as well as household PM 2.5 concentrations.
Findings to date
Published work in this cohort showed detrimental effects of biomass fuel and health inequity on birth outcomes. Current analysis indicates high levels of household PM 2.5 being associated with cooking fuel type and infant ALRI. Lastly, we identified distinct gut and respiratory microbial communities at 6 months of age.
Future plans
This study provides an economical yet effective framework to conduct pregnancy cohort studies determining the health effects of adverse environmental exposures in low-resource countries. Future analyses in this cohort include assessing the effect of indoor PM 2.5 levels on (1) physical growth, (2) neurodevelopment, (3) age of first incidence and frequency of ALRI in infants and (4) the development of the respiratory and gut microbiome. Additional support has allowed us to investigate the effect of in utero exposure to metals on infant neurodevelopment in the first year of life.
... On the other hand, the discrepancy between the three main occupied MEs (outdoor-indoor-transport) confirmed what is indicated in recent studies (Borgini et al., 2015;Hsu et al., 2020;Ozler et al., 2018). Since the transport and indoor MEs revealed a similar level of suspended fine particulates, outdoor MEs are less polluted (18.3 µg⁄m 3 ) and show an exceedance of the WHO's standards by 83%, contrary to what was seen in 2018, when the atmospheric annual average concentrations of PM 2.5 in Beirut, Lebanon, for a period of 12 months exceeded the World Health Organization's standards by 200% (Farah et al., 2018). ...
The weak potential of using the sole outdoor concentrations to represent personal exposure to PM2.5 is confirmed by the literature; therefore, it is important to account for a person’s movements over time when estimating the short-term personal air pollution exposure within different microenvironments (MEs). This study is an example of applying an assessment method of the exposure to PM2.5 in different microenvironments at different temporal scales. A low-cost particle counter (the Dylos 1700) was used; its performance was validated in comparison with equivalent instruments such the SidePak AM520 Personal Aerosol Monitor (R2 = 0.89). This validation also provided a function to convert measured particle number concentrations (PNCs) into calculated particle mass concentrations. The 150 profiles that was collected on a minute-by-minute basis regarding PM2.5 concentration from December 2018 to May 2021 highlight the influence of individual activities and contextual factors on the air quality, so that Lebanon’s annual PM2.5 mean (24.2 µg⁄m3) is 142% higher than the World Health Organization (WHO) annual mean guideline (10 µg⁄m3). Winter is the most polluted period due to the increased application of space heating devices. Additionally, the occurrence of dusty winds during the spring period leads to the elevated levels of dispersed PM2.5. Simultaneously, the rural zones are more polluted than urban ones due to the usage of more traditional heating equipment, in addition to the usage of chemical products like pesticides and fertilizers in agricultural activities in such areas. Furthermore, the (outdoor–indoor–transport) MEs indicate that the transport and indoor MEs have similar levels of suspended fine particulates, while outdoor MEs are less polluted. Studies based on the personal exposure to PM2.5 were generally applied on specific and limited places such as schools, workplaces, or residences. The study aims to shed light on the modern method in an attempt to estimate the personal exposure to PM2.5 and to inspire similar studies to achieve the maximum efficiency.
... Several studies used LCPMS to assess 24-h exposures of the general public or a certain subpopulation with specific characteristics. One study assessed personal PM2.5 exposure levels of travelers in SEA using Plantower sensors [99]. Their exposure levels depended on the microenvironments they stayed in and the direct PM2.5 sources they encountered, e.g., 32.8 μg/m 3 in the port/station and 29.6 μg/m 3 in the cafe/pub/restaurant. ...
... Their exposure levels depended on the microenvironments they stayed in and the direct PM2.5 sources they encountered, e.g., 32.8 μg/m 3 in the port/station and 29.6 μg/m 3 in the cafe/pub/restaurant. The maximum exposure (1142 μg/m 3 ) happened in an outdoor cafe/pub/restaurant with tobacco smoke, and the second-highest (525 μg/m 3 ) was also in a cafe/pub/restaurant, but indoors, with cigarettes and hookah smoke [99]. However, the mean values of PM2.5 exposures were one order of magnitude lower; these travelers experienced mean PM2.5 levels of 9.6, 14.7, 16.5, 10.9, and 16.0 μg/m 3 in Thailand, Cambodia, Singapore, Taiwan, and Hong Kong, respectively. ...
The low-cost and easy-to-use nature of rapidly developed PM2.5 sensors provide an opportunity to bring breakthroughs in PM2.5 research to resource-limited countries in Southeast Asia (SEA). This review provides an evaluation of the currently available literature and identifies research priorities in applying low-cost sensors (LCS) in PM2.5 environmental and health research in SEA. The research priority is an outcome of a series of participatory workshops under the umbrella of the International Global Atmospheric Chemistry Project-Monsoon Asia and Oceania Networking Group (IGAC-MANGO). A literature review and research prioritization are conducted with a transdisciplinary perspective of providing useful scientific evidence in assisting authorities in formulating targeted strategies to reduce severe PM2.5 pollution and health risks in this region. The PM2.5 research gaps that could be filled by LCS application are identified in five categories: source evaluation, especially for the distinctive sources in the SEA countries; hot spot investigation; peak exposure assessment; exposure-health evaluation on acute health impacts; and short-term standards. The affordability of LCS, methodology transferability, international collaboration, and stake-holder engagement are keys to success in such transdisciplinary PM2.5 research. Unique contributions to the international science community and challenges with LCS application in PM2.5 research in SEA are also discussed. Citation: Lung, S.-C.C.; Thi Hien, T.; Cambaliza, M.O.L.; Hlaing, O.M.T.; Oanh, N.T.K.; Latif, M.T.; Lestari, P.; Salam, A.; Lee, S.-Y.; Wang, W.-C.V.; et al. Research Priorities of Applying Low-Cost PM2.5 Sensors in Southeast Asian Countries. Int. J. Environ. Res.
... Regarding other personal exposure devices, it was reported from a person who traveled from the USA to Southeast Asian countries using a low-cost monitor with a Plantower PMS 3003 PM2.5 sensor that the 1 h mean PM2.5 at airports and stations were 32.8 µg/ m 3 , at cafés and public restaurants they were 29.6 µg/ m 3 , and in passenger cars it was 2.3 µg/m 3 [21]. However, that report represented only one traveler and the sensor was on the back of the subject. ...
Assessment of personal exposure to particulate matter with an aerodynamic diameter less than or 2.5 µm (PM2.5) is necessary to study the association between PM exposure and health risk. Development of a personal PM2.5 sensor or device is required for the evaluation of individual exposure level. In this study, we aimed to develop a small-sized, lightweight sensor with a global positioning system (GPS) attached that can measure PM2.5 and PM10 every second to assess continuous personal exposure levels. The participants in this study were apparently healthy housewives (n = 15) and university female teaching staff (n = 15) who live in a high PM2.5 area, Yangon, Myanmar. The average PM2.5 exposure levels during 24 h were 16.1 ± 10.0 µg/m³ in the housewives and 15.8 ± 4.0 µg/m³ in the university female teaching staff. The university female teaching staff showed high exposure concentrations during commuting hours, and had stable, relatively low concentrations at work, whereas the housewives showed short-term high exposure peaks due to differences in their lifestyles. This is the first study to show that a GPS-attached standalone PM2.5 and PM10 Sensor [PRO] can be successfully used for mobile sensing, easy use, continuous measurement, and rapid data analysis.
... The low-cost monitors were calibrated at a local monitoring station before and after the project [40]. These monitors have been evaluated and used to measure pollutants in various indoor, outdoor, and personal exposure applications previously [35,36,38,[48][49][50]. Data were characterized using descriptive statistics, graphical displays, Mann-Whitney, and Wilcoxon tests. ...
Background:
In highly polluted urban areas, personal exposure to PM2.5 and O3 occur daily in various microenvironments. Identifying which microenvironments contribute most to exposure can pinpoint effective exposure reduction strategies and mitigate adverse health impacts.
Methods:
This work uses real-time sensors to assess the exposures of children with asthma (N = 39) in Shanghai, quantifying microenvironmental exposure to PM2.5 and O3. An air cleaner was deployed in participants' bedrooms where we hypothesized exposure could be most efficiently reduced. Monitoring occurred for two 48-h periods: one with bedroom filtration (portable air cleaner with HEPA and activated carbon filters) and the other without.
Results:
Children spent 91% of their time indoors with the majority spent in their bedroom (47%). Without filtration, the bedroom and classroom environments were the largest contributors to PM2.5 exposure. With filtration, bedroom PM2.5 exposure was reduced by 75% (45% of total exposure). Although filtration status did not impact O3, the largest contribution of O3 exposure also came from the bedroom.
Conclusions:
Actions taken to reduce bedroom PM2.5 and O3 concentrations can most efficiently reduce total exposure. As real-time pollutant monitors become more accessible, similar analyses can be used to evaluate new interventions and optimize exposure reductions for a variety of populations.
... For simplicity's sake, the kernel function was set to a squared exponential (SE) covariance term to capture the spatially correlated signals coupled with another component to constrain the independent noise (Rasmussen and Williams, 2006): ...
... The final GPR was used to predict the 59 d PM 2.5 measurements of the holdout reference node (Fig. 3 step seven) following the Cholesky decomposition algorithm (Rasmussen and Williams, 2006) with the standardized predictions being transformed back to the original PM 2.5 measurement scale at the end. The back transformation was done by multiplying the predictions by the standard deviation s training (the standard deviation of the training PM 2.5 measurements) and then adding back the mean µ training (the mean of the training PM 2.5 measurements). ...
Wireless low-cost particulate matter sensor networks
(WLPMSNs) are transforming air quality monitoring by providing particulate matter (PM)
information at finer spatial and temporal resolutions. However, large-scale WLPMSN calibration and maintenance remain a challenge. The manual labor involved in initial calibration by collocation and routine recalibration is intensive. The transferability of the calibration models determined from initial collocation to new deployment sites is questionable, as calibration factors typically vary with the
urban heterogeneity of operating conditions and aerosol optical properties. Furthermore, the stability of low-cost sensors can drift or degrade over time. This study presents a simultaneous
Gaussian process regression (GPR) and simple linear regression pipeline to
calibrate and monitor dense WLPMSNs on the fly by leveraging all available
reference monitors across an area without resorting to pre-deployment
collocation calibration. We evaluated our method for Delhi, where the
PM2.5 measurements of all 22 regulatory reference and 10 low-cost
nodes were available for 59 d from 1 January to 31 March 2018
(PM2.5 averaged 138±31 µg m−3 among 22 reference
stations), using a leave-one-out cross-validation (CV) over the 22 reference
nodes. We showed that our approach can achieve an overall 30 % prediction
error (RMSE: 33 µg m−3) at a 24 h scale, and it is robust as it is
underscored by the small variability in the GPR model parameters and in the
model-produced calibration factors for the low-cost nodes among the 22-fold
CV. Of the 22 reference stations, high-quality predictions were observed for
those stations whose PM2.5 means were close to the Delhi-wide mean
(i.e., 138±31 µg m−3), and relatively poor predictions were observed for
those nodes whose means differed substantially from the Delhi-wide mean
(particularly on the lower end). We also observed washed-out local
variability in PM2.5 across the 10 low-cost sites after calibration
using our approach, which stands in marked contrast to the true wide
variability across the reference sites. These observations revealed that our
proposed technique (and more generally the geostatistical technique)
requires high spatial homogeneity in the pollutant concentrations to be
fully effective. We further demonstrated that our algorithm performance is
insensitive to training window size as the mean prediction error rate and
the standard error of the mean (SEM) for the 22 reference stations remained
consistent at ∼30 % and ∼3 %–4 %, respectively, when an
increment of 2 d of data was included in the model training. The markedly
low requirement of our algorithm for training data enables the models to
always be nearly the most updated in the field, thus realizing the algorithm's
full potential for dynamically surveilling large-scale WLPMSNs by detecting
malfunctioning low-cost nodes and tracking the drift with little latency.
Our algorithm presented similarly stable 26 %–34 % mean prediction errors
and ∼3 %–7 % SEMs over the sampling period when pre-trained
on the current week's data and predicting 1 week ahead, and therefore it is suitable
for online calibration. Simulations conducted using our algorithm suggest
that in addition to dynamic calibration, the algorithm can also be adapted
for automated monitoring of large-scale WLPMSNs. In these simulations, the
algorithm was able to differentiate malfunctioning low-cost nodes (due to
either hardware failure or under the heavy influence of local sources)
within a network by identifying aberrant model-generated calibration
factors (i.e., slopes close to zero and intercepts close to the Delhi-wide
mean of true PM2.5). The algorithm was also able to track the drift of
low-cost nodes accurately within 4 % error for all the simulation
scenarios. The simulation results showed that ∼20 reference
stations are optimum for our solution in Delhi and confirmed that low-cost
nodes can extend the spatial precision of a network by decreasing the extent
of pure interpolation among only reference stations. Our solution has
substantial implications in reducing the amount of manual labor for the
calibration and surveillance of extensive WLPMSNs, improving the spatial
comprehensiveness of PM evaluation, and enhancing the accuracy of WLPMSNs.
... Certain low-cost particulate matter (PM) sensors demonstrated satisfactory performance benchmarked against Federal Equivalent Methods (FEMs) or research-grade instruments in some 5 previous field studies (Holstius et al., 2014;Gao et al., 2015;SCAQMD, 2015a-b;Jiao et al., 2016;Kelly et al., 2017;Mukherjee et al., 2017;SCAQMD, 2017a-c;Crilley et al., 2018;Feinberg et al., 2018;Johnson et al., 2018;Zheng et al., 2018). Application-wise, low-cost PM sensors have had success in identifying urban fine particle (PM2.5, with a diameter of 2.5 µm and smaller) hotspots in Xi'an, China (Gao et al., 2015), mapping urban air quality with additional dispersion model information in Oslo, Norway (Schneider et al., 2017), monitoring smoke from prescribed fire in Colorado, US (Kelleher et 10 al., 2018), measuring a traveler's exposure to PM2.5 in various microenvironments in Southeast Asia (Ozler et al., 2018), and building up a detailed city-wide temporal and spatial indoor PM2.5 exposure profile in Beijing, China (Zuo et al., 2018). ...
Wireless low-cost particulate matter sensor networks (WLPMSNs) are transforming air quality monitoring by providing PM information at finer spatial and temporal resolutions; however, large-scale WLPMSN calibration and maintenance remain a challenge because the manual labor involved in initial calibration by collocation and routine recalibration is intensive, the transferability of the calibration models determined from initial collocation to new deployment sites is questionable as calibration factors typically vary with urban heterogeneity of operating conditions and aerosol optical properties, and the stability of low-cost sensors can develop drift or degrade over time. This study presents a simultaneous Gaussian Process regression (GPR) and simple linear regression pipeline to calibrate and monitor dense WLPMSNs on the fly by leveraging all available reference monitors across an area without resorting to pre-deployment collocation calibration. We evaluated our method for Delhi where the PM2.5 measurements of all 22 regulatory reference and 10 low-cost nodes were available in 59 valid days from 1 January 2018 to 31 March 2018 (PM2.5 averaged 138 ± 31 μg m−3 among 22 reference stations) using a leave-one-out cross-validation (CV) over the 22 reference nodes. We showed that our approach can achieve an overall 30 % prediction error (RMSE: 33 μg m−3) at a 24 h scale and is robust as underscored by the small variability in the GPR model parameters and in the model-produced calibration factors for the low-cost nodes among the 22-fold CV. We revealed that the accuracy of our calibrations depends on the degree of homogeneity of PM concentrations, and decreases with increasing local source contributions. As by-products of dynamic calibration, our algorithm can be adapted for automated large-scale WLPMSN monitoring as simulations proved its capability of differentiating malfunctioning or singular low-cost nodes within a network via model-generated calibration factors with the aberrant nodes having slopes close to 0 and intercepts close to the global mean of true PM2.5 and of tracking the drift of low-cost nodes accurately within 4 % error for all the simulation scenarios. The simulation results showed that ~20 reference stations are optimum for our solution in Delhi and confirmed that low-cost nodes can extend the spatial precision of a network by decreasing the extent of pure interpolation among only reference stations. Our solution has substantial implications in reducing the amount of manual labor for the calibration and surveillance of extensive WLPMSNs, improving the spatial comprehensiveness of PM evaluation, and enhancing the accuracy of WLPMSNs.
... Testing monitors in the environment where they will be deployed is important both because aerosol optical properties are variable and because sensor measurement ranges vary and are often not well reported by the manufacturer. Some studies use low-cost, optical monitors to evaluate personal exposure (Steinle et al., 2015;Ozler et al., 2018) or indoor air quality (Steinle et al., 2015;Mazaheri et al., 2018;Zuo et al., 2018) or explore the performance of these monitors (Jiao et al., 2016;Feinberg et al., 2018;Jayaratneet al., 2018;Johnson et al., 2018;Zheng et al., 2018) but very few authors have both calibrated and evaluated their monitor to ensure data validity in their deployment location and subsequently used the sensors to evaluate indoor air quality and personal exposure . The aims of this study were two-fold: 1) to determine the best strategy for calibrating the monitors and to better quantify monitor error and 2) to apply this calibration to assess the impact of air filtration on indoor air quality and personal exposure. ...
... Additional details about the monitors can be found on our open-source webpage (dukearc.com). These monitors have been used to monitor personal exposure and trash burning emissions in previous publications (Ozler et al., 2018;Vreeland et al., 2018;Zhang et al., 2019). ...
Portable air cleaners are increasingly used in polluted areas in an attempt to reduce human exposure, however there has been limited work characterizing their effectiveness at reducing exposure. With this in mind, we recruited forty‐three children with asthma from suburban Shanghai and deployed air cleaners (with HEPA and activated carbon filters) in their bedrooms. During both 2‐week filtration and non‐filtration periods, low‐cost PM2. 5 and O3 air monitors were used to measure pollutants indoors, outdoors, and for personal exposure. Indoor PM2. 5 concentrations were reduced substantially with the use of air cleaners, from 34±17 µ g m⁻³ to 10±8 µ g m⁻³, with roughly 80% of indoor PM2. 5 estimated to come from outdoor sources. Personal exposure to PM2. 5 was reduced from 40±17 µ g m⁻³ to 25±14 µ g m⁻³.
The more modest reductions in personal exposure and high contribution of outdoor PM2. 5 to indoor concentrations highlight the need to reduce outdoor PM2. 5 and/or to clean indoor air in multiple locations. Indoor O3 concentrations were generally low (mean=8±4 ppb), and no significant difference was seen by filtration status. The concentrations of pollutants, and the air cleaner effectiveness were highly variable over time and across homes, highlighting the usefulness of real‐time air monitors for understanding individual exposure reduction strategies.
