The effects of meteorological parameters on PM10: Health impacts assessment using AirQ+ model and prediction by an artificial neural network (ANN)

Fallahizadeh, Saeid; Kermani, Majid; Esrafili, Ali; Asadgol, Zahra; Gholami, Mitra

doi:10.1016/j.uclim.2021.100905

Cited by 19 publications

(8 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other studies also evaluated the health effects caused by exposure to pollutants in different parts of the world using AirQ+ [31,[50][51][52][53][54]. Kliengchuay et al [50] reported 125, 27, and 26 deaths caused by COPD, IHD, and stroke due to long-term exposure to PM 2.5 in Ratchaburi, Thailand, where the annual mean was 26.9 ± 18.7 µg m −3 .…”

Section: Discussion Of Health Outcomes Resultsmentioning

confidence: 99%

“…The optional input data included latitude, longitude, and location identification. We assessed different health-related results using this software tool, including the attributable proportion of cases, the number of attributable cases, the number of attributable cases per 100,000 population at risk, the proportion of cases in the pollutant concentration range, and the cumulative distribution by air pollutant concentration [16,31,32]. We also used Origin (version 2020) to plot bar charts for the attributable cases of mortality and hospital admissions.…”

Section: Data Sampling and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Health Risk Assessment of Exposure to Air Pollutants Exceeding the New WHO Air Quality Guidelines (AQGs) in São Paulo, Brazil

Wikuats

Nogueira

Squizzato

et al. 2023

IJERPH

View full text Add to dashboard Cite

We applied the AirQ+ model to analyze the 2021 data within our study period (15 December 2020 to 17 June 2022) to quantitatively estimate the number of specific health outcomes from long- and short-term exposure to atmospheric pollutants that could be avoided by adopting the new World Health Organization Air Quality Guidelines (WHO AQGs) in São Paulo, Southeastern Brazil. Based on temporal variations, PM2.5, PM10, NO2, and O3 exceeded the 2021 WHO AQGs on up to 54.4% of the days during sampling, mainly in wintertime (June to September 2021). Reducing PM2.5 values in São Paulo, as recommended by the WHO, could prevent 113 and 24 deaths from lung cancer (LC) and chronic obstructive pulmonary disease (COPD) annually, respectively. Moreover, it could avoid 258 and 163 hospitalizations caused by respiratory (RD) and cardiovascular diseases (CVD) due to PM2.5 exposure. The results for excess deaths by RD and CVD due to O3 were 443 and 228, respectively, and 90 RD hospitalizations due to NO2. Therefore, AirQ+ is a useful tool that enables further elaboration and implementation of air pollution control strategies to reduce and prevent hospital admissions, mortality, and economic costs due to exposure to PM2.5, O3, and NO2 in São Paulo.

show abstract

Section: Discussion Of Health Outcomes Resultsmentioning

confidence: 99%

Section: Data Sampling and Analysismentioning

confidence: 99%

Health Risk Assessment of Exposure to Air Pollutants Exceeding the New WHO Air Quality Guidelines (AQGs) in São Paulo, Brazil

Wikuats

Nogueira

Squizzato

et al. 2023

IJERPH

View full text Add to dashboard Cite

show abstract

“…AirQ+ software incorporates the default RR values found in meta‐analysis research. The linear‐log approach is employed to evaluate the RR of various concentrations 35 …”

Section: Methodsmentioning

confidence: 99%

Indoor environmental quality in naturally ventilated schools of a dusty region: Excess health risks and effect of heating and desert dust transport

et al. 2022

View full text Add to dashboard Cite

Indoor air quality (IAQ) is impacted by polluted outdoor air in naturally ventilated schools, especially in places where both anthropogenic and natural sources of ambient air pollution exist. CO2, PM2.5, PM10, temperature, relative humidity (RH), and noise were measured in five naturally ventilated primary schools in City of Sanliurfa, in a dusty region of Turkey, Southeast Anatolia. Excess risk levels were estimated for particulate matter. Investigation was conducted through an educational year including two seasons in terms of anthropogenic effect, that is, heating/non‐heating, and natural effect, that is, desert dust transport/non‐dust transport. The median CO2 concentration was measured to be >1000 ppm in all seasons/schools. Temperature and RH fell out of the comfort zone in October–December, during which pollutant concentrations were considerably increased, specifically in November, that heating and dust transport periods coincide. The overall mean indoor PM10 and PM2.5 levels were 58 and 31.8 μg/m3, respectively. Risk assessment indicate that both short (incidence of asthma symptoms in asthmatic children) and long‐term (prevalence of bronchitis) effects are considerable with 10.9 (2.4–19.6)% and 19.5 (2.2–38.8)%, respectively. The findings suggest that mechanical ventilation retrofitting with particle filtration is needed to mitigate potential negative health consequences on children.

show abstract

“…This neural architecture embodies multiple layers, wherein each layer contains multiple perceptron's. Neural networks utilize specialized algorithms to effectively process and analyze data, employing intricate computational techniques to extract meaningful insights (Fallahizadeh, Kermani et al 2021, Nguyen, Chuang et al 2021. Perceptron neural networks are fundamental and highly significant in the domain of artificial neural networks (Rezaei, Jafari et al 2022).…”

Section: -3-mlp Neural Networkmentioning

confidence: 99%

Using Artificial Neural Network for Forecasting PM2.5 Levels in a Prominent Industrial and Historically Significant Urban Center in Iran: Evaluating Health Implications

bardshahi,

Jaafarzadeh,

Tabatabaie

et al. 2023

Preprint

View full text Add to dashboard Cite

There has been a scarcity of comprehensive studies that aim to predict both the levels of PM2.5 particles and their corresponding impacts on human health. To fill this gap, we developed an artificial neural network model and AirQ+ software to predict PM2.5 concentrations and assess their associated health effects. The ANN model utilized five distinct input parameters, specifically wind velocity, wind orientation, atmospheric temperature, relative moisture content, and PM2.5 levels, observed within the time frame of 2018 to 2019. The concealed stratum comprised a total of ten neurons, in addition to the presence of an output layer. The MLP neural network demonstrated strong correlations at each stage: 0.908 (training), 0.910 (validation), 0.914 (testing), and 0.907 overall. The RMSE was determined as 6.52 µg/m3 when evaluating the neural network, thus indicating the notable predictive precision exhibited by the multilayer perceptron (MLP) neural network when forecasting the concentration of PM2.5 particles. The AirQ+ software, created by the World Health Organization (WHO), was employed to assess the magnitude and consequences of PM2.5 concentrations. The average concentration of PM2.5 particles throughout the duration of the study was recorded as 26.5 μg/m3, which exceeds the recommended limit provided by the WHO by a factor of 5.3. The study estimated the proportions and numbers of deaths attributed to various conditions. Specifically, chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), lung cancer (LC), stroke, and all-cause mortality accounted for approximately 11.67%, 15.02%, 13.25%, 15.225%, and 9.45% respectively. The estimated number of deaths were 19,195 for all causes, 10,063 for COPD, 564 for IHD, and 1,063 for lung cancer. The findings of this investigation demonstrate a high degree of reliability in the methodologies utilized. Through the utilization of these approaches, individuals in positions of authority and responsibility can aptly evaluate the cost-benefit analysis, leading to a reduction in human casualties and mitigating the economic burdens on society.

show abstract

The effects of meteorological parameters on PM10: Health impacts assessment using AirQ+ model and prediction by an artificial neural network (ANN)

Cited by 19 publications

References 54 publications

Health Risk Assessment of Exposure to Air Pollutants Exceeding the New WHO Air Quality Guidelines (AQGs) in São Paulo, Brazil

Health Risk Assessment of Exposure to Air Pollutants Exceeding the New WHO Air Quality Guidelines (AQGs) in São Paulo, Brazil

Indoor environmental quality in naturally ventilated schools of a dusty region: Excess health risks and effect of heating and desert dust transport

Using Artificial Neural Network for Forecasting PM2.5 Levels in a Prominent Industrial and Historically Significant Urban Center in Iran: Evaluating Health Implications

Contact Info

Product

Resources

About