Spatio-Temporal Variation of Particulate Matter(PM<sub>2.5</sub>) Concentrations and Its Health Impacts in a Mega City, Delhi in India

Gorai, Amit Kumar; Tchounwou, Paul B.; Biswal, Shanti Swarup; Tuluri, Francis

doi:10.1177/1178630218792861

Cited by 58 publications

(33 citation statements)

References 19 publications

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“…The relatively small size of PMs, especially the PMs with a diameter smaller than 2.5 um (PM2.5) allows them to be inhaled into deeper respiratory tracts, even into the circulatory system, and to induce detrimental health effects within the cardiopulmonary system (Brook et al, 2004;Almendra et al, 2017). While developed countries, such as the United States and Japan, displayed acceptable levels of ambient PM, some areas of the world displayed relatively high levels, far beyond the healthy levels set by the World Health Organization (Puett et al, 2011;Zhou et al, 2015;Gorai et al, 2018;Khan et al, 2018), posing a significant threat to human health. PM exposure has been associated with cardiovascular diseases (Ensor et al, 2013;Sohn et al, 2016) and cardiovascularrelated mortality (Zhang C. et al, 2018;Hvidtfeldt et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Real-Ambient Particulate Matter Exposure-Induced Cardiotoxicity in C57/B6 Mice

Cui

Shi

et al. 2020

Front. Pharmacol.

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Section: Introductionmentioning

confidence: 99%

Real-Ambient Particulate Matter Exposure-Induced Cardiotoxicity in C57/B6 Mice

Cui

Shi

et al. 2020

Front. Pharmacol.

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“…Yet, the transferability of these pre-determined calibrations at collocation sites to new deployment sites is questionable as cali-bration factors typically vary with operating conditions such as PM mass concentrations, relative humidity (RH), temperature, and aerosol optical properties (Holstius et al, 2014;Austin et al, 2015;Wang et al, 2015;Lewis and Edwards, 2016;Crilley et al, 2018;Jayaratne et al, 2018;Zheng et al, 2018). Complicating this further, the pre-generated calibration curves may only apply for a short term as the stability of low-cost sensors can develop drift or degrade over time (Lewis and Edwards, 2016;Jiao et al, 2016;Hagler et al, 2018). Routine recalibrations which require frequent transit of the deployed sensors between the field and the reference sites are not only too labor intensive for a large-scale network but also still cannot address the impact of urban heterogeneity of ambient conditions on calibration models (Kizel et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi

Zheng

Bergin

Sutaria³

et al. 2019

Atmos. Meas. Tech.

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Abstract. 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.

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“…The Air Quality Index (AQI) of the US Environmental Protection Agency (US EPA) is a widely used index which gives details about the daily air quality status [35]. In the present work, with the principal aim to investigate the possible relationship between the configured PM burden (mass, chemical composition) and the corresponding health effects, the USEPA Air Quality Index (AQI) for PM 2.5 was applied.…”

Section: Health Riskmentioning

confidence: 99%

“…In the present work, with the principal aim to investigate the possible relationship between the configured PM burden (mass, chemical composition) and the corresponding health effects, the USEPA Air Quality Index (AQI) for PM 2.5 was applied. Details about the specific methodology, the breakpoints for the PM 2.5 concentrations, the ranges of the PM 2.5 -AQI categories and the possible health consequences are given in EPA 1999, [36] and Gorai et al [35]. Dimitriou et al [37] found that AQI in Athens is mainly affected by PM (by 72%) and only 28% by ozone with the contribution of other pollutants being negligible.…”

Section: Health Riskmentioning

confidence: 99%

Differentiation of the Athens Fine PM Profile during Economic Recession (March of 2008 Versus March of 2013): Impact of Changes in Anthropogenic Emissions and the Associated Health Effect

et al. 2020

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Despite the various reduction policies that have been implemented across Europe in the past few years, Particulate Matter (PM) exceedances continue to be recorded. Therefore, with the principal aim to clarify the complex association between emissions and fine particles levels, this work evaluates the impact of the anthropogenic contribution to the fine PM chemical profile. The fieldwork was conducted during March in 2008 and 2013 and covers the periods before and during the economic recession. The experimental data were analyzed in parallel with the emissions from the Flexible Emission Inventory for Greece and the Greater Athens Area (FEI-GREGAA). The differentiation of the mass closure results’ and the aerosols’ character is also discussed in combination with the calculated PM2.5-Air Quality Indexes. The peak in the PM load and the Particulate Organic Matter (POM) component was recorded in 2013, corresponding to the enhancement of the anthropogenic input. Although the monitoring location is traffic-impacted, the sector of heating, from both wood burning and fossil fuel, proved to be the driving force for the configuration of the obtained PM picture. Especially in 2013, its contribution was two times that of traffic. Finally, the low wind speed values led to the deterioration of the air quality, especially for the sensitive groups.

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Spatio-Temporal Variation of Particulate Matter(PM_2.5) Concentrations and Its Health Impacts in a Mega City, Delhi in India

Cited by 58 publications

References 19 publications

Real-Ambient Particulate Matter Exposure-Induced Cardiotoxicity in C57/B6 Mice

Real-Ambient Particulate Matter Exposure-Induced Cardiotoxicity in C57/B6 Mice

Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi

Differentiation of the Athens Fine PM Profile during Economic Recession (March of 2008 Versus March of 2013): Impact of Changes in Anthropogenic Emissions and the Associated Health Effect

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Spatio-Temporal Variation of Particulate Matter(PM2.5) Concentrations and Its Health Impacts in a Mega City, Delhi in India

Cited by 58 publications

References 19 publications

Real-Ambient Particulate Matter Exposure-Induced Cardiotoxicity in C57/B6 Mice

Real-Ambient Particulate Matter Exposure-Induced Cardiotoxicity in C57/B6 Mice

Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi

Differentiation of the Athens Fine PM Profile during Economic Recession (March of 2008 Versus March of 2013): Impact of Changes in Anthropogenic Emissions and the Associated Health Effect

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Spatio-Temporal Variation of Particulate Matter(PM_2.5) Concentrations and Its Health Impacts in a Mega City, Delhi in India