Transport of the smoke plume from Chiado’s fire in Lisbon (Portugal) sensed by atmospheric electric field measurements

Conceição, Ricardo; Melgão, Marta; Silva, Hugo; Nicoll, Keri; Harrison, Richard G.; Reis, A. Heitor

doi:10.1007/s11869-015-0337-4

Cited by 15 publications

(8 citation statements)

References 18 publications

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“…A range of other approaches also have been employed to identify days with exposure to burning, or haze, including a doubling of total suspended particulates (mean = 56.9 μg/m 3 [49]), the extent of discoloration in the sky [36], a threshold of 80 μg/m 3 to indicate 'unhealthy' levels [50], and exposure in the month of March [46]. Other studies of exposure to fires have employed software to track polluted air mass trajectories based on meteorological data (e.g., [51]).…”

Section: Discussionmentioning

confidence: 99%

Ambient particulate matter and biomass burning: an ecological time series study of respiratory and cardiovascular hospital visits in northern Thailand

et al. 2020

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Background: Exposure to particulate matter (PM) emitted from biomass burning is an increasing concern, particularly in Southeast Asia. It is not yet clear how the source of PM influences the risk of an adverse health outcome. The objective of this study was to quantify and compare health risks of PM from biomass burning and non-biomass burning sources in northern Thailand. Methods: We collected ambient air pollutant data (PM with a diameter of < 10 μm [PM 10 ], PM 2.5 , Carbon Monoxide [CO], Ozone [O 3 ], and Nitrogen Dioxide [NO 2 ]) from ground-based monitors and daily outpatient hospital visits in Thailand during 2014-2017. Outpatient data included chronic lower respiratory disease (CLRD), ischaemic heart disease (IHD), and cerebrovascular disease (CBVD). We performed an ecological time series analysis to evaluate the association between daily air pollutants and outpatient visits. We used the 90th and 95th percentiles of PM 10 concentrations to determine days of exposure to PM predominantly from biomass burning. Results: There was significant intra annual variation in PM 10 levels, with the highest concentrations occurring during March, coinciding with peak biomass burning. Incidence Rate Ratios (IRRs) between daily PM 10 and outpatient visits were elevated most on the same day as exposure for CLRD = 1.020 (95% CI: 1.012 to 1.028) and CBVD = 1.020 (95% CI: 1.004 to 1.035), with no association with IHD = 0.994 (95% CI: 0.974 to 1.014). Adjusting for CO tended to increase effect estimates. We did not find evidence of an exposure response relationship with levels of PM 10 on days of biomass burning. Conclusions: We found same-day exposures of PM 10 to be associated with certain respiratory and cardiovascular outpatient visits. We advise implementing measures to reduce population exposures to PM wherever possible, and to improve understanding of health effects associated with burning specific types of biomass in areas where such large-scale activities occur.

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Section: Discussionmentioning

confidence: 99%

Ambient particulate matter and biomass burning: an ecological time series study of respiratory and cardiovascular hospital visits in northern Thailand

et al. 2020

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“…The analog records of the electrograph were digitalized afterwards (Serrano, 2010). Further details on the dataset can be found in Serrano et al (2006), Silva et al (2014) and Conceição et al (2015). Measurements with similar devices were made worldwide (e.g.…”

Section: Datamentioning

confidence: 99%

Aerosol hygroscopic growth and the dependence of atmospheric electric field measurements with relative humidity

Silva

Conceição

Wright³

et al. 2015

Journal of Aerosol Science

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A simple formulation is developed to model the influence of the aerosol hygroscopic growth in the dependence of the atmospheric electric field measurements with relative humidity. The formulation uses the Petters and Kreidenweis's model for the hygroscopic growth factor of aerosols with relative humidity and assumes that the ion-aerosol attachment coefficient is linearly proportional to the particle radius according to Gunn's calculation. A formula which describes the atmospheric electric field increase with relative humidity in the regime expected for the aerosols to grow hygroscopically is found; between 60% and 90%. It also relates the microphysical parameter of aerosol hygroscopicity, κ, with the macrophysical measure of the atmospheric electric field. Historical data of atmospheric electric field and relative humidity recorded in the meteorological station of Portela (near Lisbon airport, Portugal) are used to fit the model. The electrical measurements were done with a Benndorf electrograph and the 1980-1990 period was considered. Due to the high pollution levels the atmospheric electric field measurements were divided in four wind sectors, NW, NE, SE, and SW. The sector least affected by pollutant aerosols, NW, was used in the fitting and the goodness found is r 2 $ 0.97, the aerosol concentration number is $ 3280 cm À 3 and the hygroscopic growth parameter κ$0.094. These are very reasonable values consistent with an urban environment, which typically has high aerosol number concentration with small hygroscopicity. The limitations of the model are presented throughout the sections.

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“…The duration of these energy deficiency periods can go from a few minutes to an entire day, depending on various atmospheric parameters such as size and depth of clouds, wind velocity and direction, and aerosols (concentration and type). The latter, while less frequent, can also significantly affect CSP energy production, as during desert dust storms [3] or severe air pollution events [4].…”

Section: Introductionmentioning

confidence: 99%

Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants

et al. 2021

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To contribute for improved operational strategies of concentrating solar power plants with accurate forecasts of direct normal irradiance, this work describes the use of several post-processing methods on numerical weather prediction. Focus is given to a multivariate regression model that uses measured irradiance values from previous hours to improve next-hour predictions, which can be used to refine daily strategies based on day-ahead predictions. Short-term forecasts provided by the Integrated Forecasting System, the global model from the European Centre for Medium-Range Weather Forecasts (ECMWF), are used together with measurements in southern Portugal. As a nowcasting tool, the proposed regression model significantly improves hourly predictions with a skill score of z0.84 (i.e. an increase of z27.29% towards the original hourly forecasts). Using previous-day measured availability to improve next-day forecasts, the model shows a skill score of z0.78 (i.e. an increase of z6% towards the original forecasts), being further improved if larger sets of data are used. Through a power plant simulator (i.e. the System Advisor Model), a preliminary economic analysis shows that using improved hourly predictions of electrical energy allows to enhance a power plant's profit in z0.44 MV/year, as compared with the original forecasts. Operational strategies are proposed accordingly.

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Transport of the smoke plume from Chiado’s fire in Lisbon (Portugal) sensed by atmospheric electric field measurements

Cited by 15 publications

References 18 publications

Ambient particulate matter and biomass burning: an ecological time series study of respiratory and cardiovascular hospital visits in northern Thailand

Ambient particulate matter and biomass burning: an ecological time series study of respiratory and cardiovascular hospital visits in northern Thailand

Aerosol hygroscopic growth and the dependence of atmospheric electric field measurements with relative humidity

Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants

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