| 1. |
- Darynova, Zhuldyz, et al.
(författare)
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Reliability and stability of a statistical model to predict ground-based PM 2.5 over 10 years in Karachi, Pakistan, using satellite observations
- 2023
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Ingår i: Air Quality, Atmosphere and Health. - : Springer Science and Business Media LLC. - 1873-9318 .- 1873-9326. ; 16:4, s. 669-679
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the complex mechanisms of climate change and its environmental consequences requires the collection and subsequent analysis of geospatial data from observations and numerical modeling. Multivariable linear regression and mixed-effects models were used to estimate daily surface fine particulate matter (PM2.5) levels in the megacity of Pakistan. The main parameters for the multivariable linear regression model were the 10-km-resolution satellite aerosol optical depth (AOD) and daily averaged meteorological parameters from ground monitoring (temperature, dew point, relative humidity, wind speed, wind direction, and planetary boundary layer height). Ground-based PM2.5 was measured in two stations in the city, Korangi (industrial/residential) and Tibet Center (commercial/residential). The initial linear regression model was modified using a stepwise selection procedure and adding interaction parameters. Finally, the modified model showed a strong correlation between the PM2.5–satellite AOD and other meteorological parameters (R2 = 0.88–0.92 and p-value = 10−7 depending on the season and station). The mixed-effect technique improved the model performance by increasing the R2 values to 0.99 and 0.93 for the Korangi and Tibet Center sites, respectively. Cross-validation methods were used to confirm the reliability of the model to predict PM2.5 after 10 years.
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| 2. |
- Morawska, Lidia, et al.
(författare)
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The state of science on severe air pollution episodes : Quantitative and qualitative analysis
- 2021
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Ingår i: Environment International. - : Elsevier BV. - 1873-6750 .- 0160-4120. ; 156, s. 106732-106732
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Tidskriftsartikel (refereegranskat)abstract
- Severe episodic air pollution blankets entire cities and regions and have a profound impact on humans and their activities. We compiled daily fine particle (PM2.5) data from 100 cities in five continents, investigated the trends of number, frequency, and duration of pollution episodes, and compared these with the baseline trend in air pollution. We showed that the factors contributing to these events are complex; however, long-term measures to abate emissions from all anthropogenic sources at all times is also the most efficient way to reduce the occurrence of severe air pollution events. In the short term, accurate forecasting systems of such events based on the meteorological conditions favouring their occurrence, together with effective emergency mitigation of anthropogenic sources, may lessen their magnitude and/or duration. However, there is no clear way of preventing events caused by natural sources affected by climate change, such as wildfires and desert dust outbreaks.
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