Characteristics of Particulate Pollution (PM2.5 and PM10) and Their Spacescale-Dependent Relationships with Meteorological Elements in China

Li, Xiaodong; Chen, Xuwu; Zeng, Guangming; León, Tomás Munilla; Liang, Jie; Chen, Gaojie; Yuan, Xinliang

doi:10.3390/su9122330

Cited by 44 publications

(14 citation statements)

References 52 publications

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“…The ratio between PM 10 and PM 2.5 was generally constant and was equal about 1.1, and the ratio between PM 2.5 and PM 1 was at the level of 1.5. Such simple relationships might be not adequate for ambient air monitoring, where PM mass ratios depend on the pollutant sources and may change during the year [47,48].…”

Section: Measurements Resultsmentioning

confidence: 99%

Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements

Badura

Batog²,

Drzeniecka-Osiadacz

et al. 2019

SN Appl. Sci.

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The article presents comparison of regression methods used to obtain calibration formulas for low-cost optical particulate matter sensors. Data for analysis were taken from 1-year collocation study of PMS7003 sensors (Plantower) with researchgrade instrument TEOM 1400a. The PM 2.5 fraction was considered in this study. The results of measurements showed that PMS7003 was characterized by high reproducibility between units (coefficient of variation was lower than 10%), but the raw sensor outputs significantly overestimated PM 2.5 concentrations. Data analysis revealed that simple univariate models were sufficient to obtain a good fitting quality to TEOM data; however, the best results were achieved for raw PM 1 outputs (R 2 ≈ 0.81). The fitting quality was improved when multi-variable equations were examined (R 2 ≈ 0.84). The addition of temperature and relative humidity in the models was also beneficial (R 2 ≈ 0.87). Stepwise selection algorithm was used to choose the best subset of variables in the model. The results of that method were compared with "all possible regression" approach, demonstrating the convenience of stepwise regression. Data from Plantower sensor were also used for training of artificial neural network. That algorithm proved to be very effective for fitting data from one sensor (R 2 ≈ 0.9), but it was susceptible to deviations in the data from the other units. In general, regression analysis proved to be useful for sensor systems for ambient particulate matter measurements.

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Section: Measurements Resultsmentioning

confidence: 99%

Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements

Badura

Batog²,

Drzeniecka-Osiadacz

et al. 2019

SN Appl. Sci.

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show abstract

“…In the next line, we update the value of PM_Image by multiplying the division result with 55.40. This 55.40 refers to the average PM 2.5 concentration of 55.40 µg/m 3 if the relative humidity of the air is above 87% [46]. 2).…”

Section: Neural Network Representationmentioning

confidence: 99%

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Kabir

Islam

Hossain

et al. 2020

Sensors

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Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

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“…The IPCC report [2] shows that an increase in the frequency of extreme rainfall is likely to occur in most areas during the 21st century with different emissions scenarios. Recent studies by [3,4] reported that human activities played a leading role in increasing climate change impacts. The projected results of these changes include flooding, damage to crops, soil erosion, adverse effects on surface and groundwater quality, water scarcity, water contamination, disease outbreak, loss of properties, disruption of the settlement, and other socio-economic challenges [5].…”

Section: Introductionmentioning

confidence: 99%

Potential Impacts of Climate Change on Extreme Weather Events in the Niger Delta Part of Nigeria

et al. 2020

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The Niger Delta is the most climate-vulnerable region in Nigeria. Flooding events are recorded annually in settlements along the River Niger and its tributaries, inundating many towns and displacing people from their homes. In this study, climate change impacts from extreme meteorological events over the period 2010–2099 are predicted and analyzed. Four coupled model intercomparison project phase 5 (CMIP5) global climate models (GCMs) under respectively concentration pathways (RCP4.5 and RCP8.5) emission scenarios were used for climate change predictions. Standardized precipitation indices (SPI) of 1-month and 12-month time steps were used for extreme event assessment. Results from the climate change scenarios predict an increase in rainfall across all future periods and under both emission scenarios, with the highest projected increase during the last three decades of the century. Under the RCP8.5 emission scenario, the rainfall at Port Harcourt and Yenagoa Stations is predicted to increase by about 2.47% and 2.62% while the rainfall at Warri Station is predicted to increase by about 1.39% toward the end of the century. The 12-month SPI under RCP4.5 and RCP8.5 emission scenarios predict an exceedance in the extreme wet threshold (i.e., SPI > 2) during all future periods and across all study locations. These findings suggest an increasing risk of flooding within the projected periods. The finding can be useful to policymakers for the formulation and planning of flood mitigation and adaptation measures.

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Characteristics of Particulate Pollution (PM2.5 and PM10) and Their Spacescale-Dependent Relationships with Meteorological Elements in China

Cited by 44 publications

References 52 publications

Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements

Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Potential Impacts of Climate Change on Extreme Weather Events in the Niger Delta Part of Nigeria

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