Impact of 4th of July Fireworks on Spatiotemporal PM2.5 Concentrations in California Based on the PurpleAir Sensor Network: Implications for Policy and Environmental Justice

Mousavi, Amirhosein; Yuan, Yali; Masri, Shahir; Barta, Greg; Wu, Jun

doi:10.3390/ijerph18115735

Cited by 24 publications

(14 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some scholars believe that it may affect the stock market, the performances of companies, and so on [42,43]. At the theoretical level, the basis for policy evaluation has been relatively rich, but a review of the literature shows that environmental policies are not sufficiently evaluated [44,45]. Although the interview system has been applied in many fields, research on it has been qualitative.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the Policy Effect of China’s Environmental Interview System for Effective Air Quality Governance

Jin

Sumaila

Yin

et al. 2021

IJERPH

View full text Add to dashboard Cite

The Ministry of Ecology and Environment of the People’s Republic of China formally proposed an environmental interview system in May 2014, which applies pressure on local governments to fulfill their responsibility toward environmental protection by conducting face-to-face public interviews with their officials. In this paper, 48 cities that were publicly interviewed from 2014–2020 were considered the experimental group and 48 cities surrounding them were the control group. First, the dynamic panel model is applied to initially determine the effect of the policy. Then, a regression discontinuity method (Sharp RD) is used to analyze the short-term and long-term effects and compare the reasons for the differences observed among the estimates of various types of samples. Finally, a series of robustness tests were also conducted. The results show that the environmental interview system can improve air quality. However, because an emergency short-term local governance system exists at present, the governance effect is not long-term and, therefore, not sustainable. Therefore, it suggests that the government should continue to improve the environmental interview system, establish an optimal environmental protection incentive mechanism, and encourage local governments to implement environmental protection policies effectively in the long term. The results of the research are of great significance to the environmental impact assessment system of the world, especially in countries with similar economic systems, which are facing a trade-off between economic growth and environmental sustainability.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of the Policy Effect of China’s Environmental Interview System for Effective Air Quality Governance

Jin

Sumaila

Yin

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Several studies have shown that Dylos may be used as a simple low-cost substitute for gravimetric analysis when measuring fine particles, such as second-hand cigarette smoke or ambient air pollution ( Semple et al , 2015 , Carvlin et al , 2017 , Ferdous et al , 2020 ). Similarly, PA has been used to measure fine particle ambient air pollution in various settings ( Mousavi et al , 2021 ). Moreover, we found the use of the Dylos counter particularly problematic since larger cotton particles (‘fluff’) tended to choke the device’s internal fan, necessitating frequent cleaning during field sampling.…”

Section: Discussionmentioning

confidence: 99%

Use of Low-Cost Particle Counters for Cotton Dust Exposure Assessment in Textile Mills in Low- and Middle-Income Countries

Nafees

Iqbal

Cullinan

et al. 2021

Annals of Work Exposures and Health

View full text Add to dashboard Cite

Objective There is a lack of consensus on methods for cotton dust measurement in the textile industry, and techniques vary between countries—relying mostly on cumbersome, traditional approaches. We undertook comparisons of standard, gravimetric methods with low-cost optical particle counters for personal and area dust measurements in textile mills in Pakistan. Methods We included male textile workers from the weaving sections of seven cotton mills in Karachi. We used the Institute of Occupational Medicine (IOM) sampler with a Casella Apex 2 standard pump and the Purple Air (PA-II-SD) for measuring personal exposures to inhalable airborne particles (n = 31). We used the Dylos DC1700 particle counter, in addition to the two above, for area-level measurements (n = 29). Results There were no significant correlations between the IOM and PA for personal dust measurements using the original (r = −0.15, P = 0.4) or log-transformed data (r = −0.32, P = 0.07). Similarly, there were no significant correlations when comparing the IOM with either of the particle counters (PA and Dylos) for area dust measurements, using the original (r = −0.07, P = 0.7; r = 0.10, P = 0.6) or log-transformed data (r = −0.09, P = 0.6; r = 0.07, P = 0.7). Conclusion Our findings show a lack of correlation between the gravimetric method and the use of particle counters in both personal and area measurements of cotton dust, precluding their use for measuring occupational exposures to airborne dust in textile mills. There continues to be a need to develop low-cost instruments to help textile industries in low- and middle-income countries to perform cotton dust exposure assessment.

show abstract

“…Some widely adopted sensors include the Plantower PMS series (module used in a variety of integrated sensor systems, such as PurpleAir), Alphasense OPC-N2, Panasonic PM2.5 sensors, NovaFitness SDS, Shinyei PPD series, Samyoung DSM series, and Sharp GP and DP series. Those sensors have been used under various scenarios, such as environmental regulation (Bi et al, 2020), hotspot detection (Mousavi et al, 2021), traffic-related studies (Amegah et al, 2021;McFarlane et al, 2021), and health assessment (Tsou et al, 2021). This change is significant in transforming environmental governance, especially in historically underserved countries or regions, where access to the air quality data is insufficient to act on pollution trends (Pope et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection

Daniels

2022

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

The low-cost sensor has changed the air quality monitoring paradigm with the capacity for efficient network expansion and community engagement. The surge in its use has sparked a new research wave in understanding its data quality. Many studies have employed field calibration to improve sensor agreement with co-located reference monitors. Yet, studies that systematically examine the performance of different calibration techniques are limited in scope and depth. This study comprehensively assessed ten widely used data techniques, namely AdaBoost, Bayesian ridge, gradient tree boosting, K-nearest neighbors, Lasso, multivariable linear regression, neural network, random forest, ridge regression, and support vector machine. We compared their performance using a standardized baseline dataset and their responses to various parameter combinations. We further assessed the training sample size effect to understand the optimal duration for field calibration for achieving good accuracy. Finally, we tested different predictor combinations to address whether the inclusion of more predictors will lead to better performance. Using baseline data, the neural network achieved the best performance, followed by the four regression-based methods, showing very consistent and stable performance. While confirming that the latest research tendency is deep learning, regression is still a viable option for studies with limited effort in parameter tuning and method selection, especially considering its computational efficiency and simplicity. The sample size effect is most evident when the sample size drops below 30%, which is equivalent to six weeks of continuously collected hourly data. Although algorithms react differently to the number of predictors, their performance was typically boosted by adding more predictors, especially the particle count and humidity. Our study not only describes an approach of sophisticated data-driven calibration for practical applications, but also provides insights into the compounding impacts of parameters, samples, and predictors in algorithm performance.

show abstract

Impact of 4th of July Fireworks on Spatiotemporal PM2.5 Concentrations in California Based on the PurpleAir Sensor Network: Implications for Policy and Environmental Justice

Cited by 24 publications

References 53 publications

Evaluation of the Policy Effect of China’s Environmental Interview System for Effective Air Quality Governance

Evaluation of the Policy Effect of China’s Environmental Interview System for Effective Air Quality Governance

Use of Low-Cost Particle Counters for Cotton Dust Exposure Assessment in Textile Mills in Low- and Middle-Income Countries

What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection

Contact Info

Product

Resources

About