Health and Economic Burden of the 2017 Portuguese Extreme Wildland Fires on Children

Wildland fires release substantial amounts of hazardous contaminants, contributing to a decline in air quality and leading to serious health risks. Thus, this study aimed to understand the contributions of the 2017 extreme wildland fires in Portugal on children health, compared to 2016 (with burned area, in accordance with the average of the previous 15 years). The impact of long-term exposure to PM10 and NO2 concentrations, associated with wildland fires, on postneonatal mortality, bronchitis prevalence, and bronchitis symptoms in asthmatic children was estimated, as well as the associated costs. The excess health burden in children attributable to exposure to PM10 and NO2, was calculated based on WHO HRAPIE relative risks. Fire emissions were obtained from the Fire INventory from NCAR (FINN). The results obtained indicate that the smoke from wildfires negatively impacts children’s lung function (PM10 exposure: increase of 320 and 648 cases of bronchitis in 2016 and 2017; NO2 exposure: 24 and 40 cases of bronchitis symptoms in asthmatic children in 2016 and 2017) and postneonatal mortality (PM10 exposure: 0.2 and 0.4 deaths in 2016 and 2017). Associated costs were increased in 2017 by around 1 million € for all the evaluated health endpoints, compared to 2016.

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Modelling African biomass burning emissions and the effect of spatial resolution

10.5194/gmd-2019-116 ◽

2019 ◽

Author(s):

Dave van Wees ◽

Guido R. van der Werf

Keyword(s):

Spatial Resolution ◽

Input Data ◽

Large Scale ◽

Sub Saharan Africa ◽

Fuel Load ◽

Burned Area ◽

Fire Emissions ◽

Continental Scale ◽

Coarse Resolution ◽

The Impact

Abstract. Large-scale fire emission estimates may be influenced by the spatial resolution of the model and input datasets used. Especially in areas with relatively heterogeneous land cover, a coarse model resolution might lead to substantial errors in estimates. In this paper, we developed a model using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations of burned area and vegetation characteristics to study the impact of spatial resolution on modelled fire emission estimates. We estimated fire emissions for sub-Saharan Africa at 500-meter spatial resolution (native MODIS burned area) for the 2002–2017 period, using a simplified version of the Global Fire Emissions Database (GFED) modelling framework, and compared this to model runs at a range of coarser resolutions (0.050°, 0.125°, 0.250°). We estimated fire emissions of 0.68 PgC yr−1 at 500-meter resolution and 0.82 PgC yr−1 at 0.25° resolution; a difference of 24 %. At 0.25° resolution, our model results were relatively similar to GFED4, which also runs at 0.25° resolution, whereas our 500-meter estimates were substantially lower. We found that lower emissions at finer resolutions are mainly the result of reduced representation errors when comparing modelled estimates of fuel load and consumption to field measurements, as part of the model calibration. Additional errors stem from the model simulation at coarse resolution and lead to an additional 0.02 PgC yr−1 difference in estimates. These errors exist due to the aggregation of quantitative and qualitative model input data; the average- or majority- aggregated values are propagated in the coarse resolution simulation and affect the model parameterization and the final result. We identified at least three error mechanisms responsible for the differences in estimates between 500-meter and 0.25° resolution simulations, besides those stemming from representation errors in the calibration process, namely: 1. biome misclassification leading to errors in parameterization, 2. errors due to the averaging of input data and the associated reduction in variability, and 3. a temporal mechanism related to the aggregation of burned area in particular. Even though these mechanisms largely neutralized each other and only modestly affect estimates at a continental scale, they lead to substantial error at regional scales with deviations up to a factor 4, and may affect large-scale estimates differently for other continents. These findings could prove valuable in improving coarse resolution models and suggest the need for increased spatial resolution in global fire emission models.

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The impact of cognitive dysfunction on mid- and long-term mortality after vascular surgery

10.21203/rs.3.rs-42677/v1 ◽

2020 ◽

Author(s):

Andras Szabo ◽

Nikoletta Czobor ◽

Adam Nagy ◽

Krisztina Toth ◽

Csaba Eke ◽

...

Keyword(s):

Vascular Surgery ◽

Cognitive Dysfunction ◽

Cox Regression ◽

Mmse Score ◽

Life Quality ◽

Relative Risks ◽

The Difference ◽

The Impact ◽

Long Term Mortality

Abstract Background: In the last decades prior studies noted the importance of frailty which is a frequently used term in perioperative risk evaluation. We investigated frailty syndrome as the psychological and socioeconomic elements of the human being. The aim of this study was assessing the importance of these factors for mortality after vascular surgery.Methods: In our prospective, observational study (ClinicalTrials.gov Identifier: NCT02224222) we examined 164 patients who underwent an elective vascular surgery between 2014 and 2017. At the point of admission they filled out a questionnaire, in this way the patients’ cognitive functions, depression and anxiety, social support and self-reported life quality were mapped. We used Cox regression and Kaplan-Meier method for relative risk calculation and survival analyses. Propensity score matching was performed to analyze the difference between patient and control, nation-wide population cohort. Effects of psychosocial factors on long term mortality were defined as primary outcome. Results: The patients mean age were 67.05 years (SD: 9.49 years). One out of four patients had some kind of cognitive impairment measured by Mini Mental State Examination with modified, more sensitive cut-off values. In univariate Cox regression higher MMSE score was associated decreased risk for all-cause mortality (OR: 0.883, 95% CI: 0.802-0.973, p=0.012). After clusters were created according to MMSE score relative risks were calculated. Even mild cognitive dysfunction could increase risk for long term mortality (AHR: 1.634, 95% CI: 1.118-2.368, p=0.009).Conclusion: Even mild cognitive dysfunction measured by the MMSE preoperatively could be an important risk factor for mortality after vascular surgery.

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Signature of tropical fires in the diurnal cycle of tropospheric CO as seen from Metop-A/IASI

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-14-26003-2014 ◽

2014 ◽

Vol 14 (19) ◽

pp. 26003-26039 ◽

Cited By ~ 1

Author(s):

T. Thonat ◽

C. Crevoisier ◽

N. A. Scott ◽

A. Chédin ◽

R. Armante ◽

...

Keyword(s):

Burned Area ◽

Tropical Region ◽

Fire Emissions ◽

Diurnal Difference ◽

Moderate Resolution ◽

Resolution Imaging ◽

Emissions Inventories ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Impact ◽

Local Emissions

Abstract. Five years (July 2007–June 2012) of CO tropospheric columns derived from the IASI hyperspectral infrared sounder onboard Metop-A are used to study the impact of fires on the concentrations of CO in the mid-troposphere. Following Chédin et al. (2005, 2008), who showed the existence of a daily tropospheric excess of CO2 quantitatively related to fire emissions, we show that tropospheric CO also displays a diurnal signal with a seasonality that is in very good agreement with the seasonal evolution of fires given by GFED3.1 (Global Fire Emission Database) emissions and MODIS (Moderate Resolution Imaging Spectroradiometer) burned area. Unlike daytime or nighttime CO fields, which mix local emissions with nearby emissions transported to the region of study, the day-night difference of CO allows to highlight the CO signal due to local fire emissions. A linear relationship is found in the whole tropical region between CO fire emissions from the GFED3.1 inventory and the diurnal difference of IASI CO (R2 ~ 0.6). Based on the specificity of the two main phases of the combustion (flaming vs. smoldering) and on the vertical sensitivity of the sounder to CO, the following mechanism is proposed to explain such a CO diurnal signal: at night, after the passing of IASI at 9.30 p.m. LT, a large amount of CO emissions from the smoldering phase is trapped in the boundary layer before being uplifted the next morning by natural and pyro-convection up to the free troposphere, where it is seen by IASI at 9.30 a.m. LT. The results presented here highlight the need for developing complementary approaches to bottom-up emissions inventories and for taking into account the specificity of both the flaming and smoldering phases of fire emissions in order to fully take advantage of CO observations.

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Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018)

Fire ◽

10.3390/fire4040074 ◽

2021 ◽

Vol 4 (4) ◽

pp. 74

Author(s):

Gonzalo Otón ◽

José Miguel C. Pereira ◽

João M. N. Silva ◽

Emilio Chuvieco

Keyword(s):

Time Series ◽

Temporal Trends ◽

European Space Agency ◽

Eastern Africa ◽

Burned Area ◽

Data Series ◽

Short Term ◽

The Impact ◽

Term Data

We present an analysis of the spatio-temporal trends derived from long-term burned area (BA) data series. Two global BA products were included in our analysis, the FireCCI51 (2001–2019) and the FireCCILT11 (1982–2018) datasets. The former was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m reflectance data, guided by 1 km active fires. The FireCCILT11 dataset was generated from Land Long-Term Data Record data (0.05°), which provides a consistent time series for Advanced Very High Resolution Radiometer images, acquired from the NOAA satellite series. FireCCILT11 is the longest time series of a BA product currently available, making it possible to carry out temporal analysis of long-term trends. Both products were developed under the FireCCI project of the European Space Agency. The two datasets were pre-processed to correct for temporal autocorrelation. Unburnable areas were removed and the lack of the FireCCILT11 data in 1994 was examined to evaluate the impact of this gap on the BA trends. An analysis and comparison between the two BA products was performed using a contextual approach. Results of the contextual Mann-Kendall analysis identified significant trends in both datasets, with very different regional values. The long-term series presented larger clusters than the short-term ones. Africa displayed significant decreasing trends in the short-term, and increasing trends in the long-term data series, except in the east. In the long-term series, Eastern Africa, boreal regions, Central Asia and South Australia showed large BA decrease clusters, and Western and Central Africa, South America, USA and North Australia presented BA increase clusters.

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Long-term Psychosocial Sequelae of Chronic Physical Disorders in Childhood

PEDIATRICS ◽

10.1542/peds.91.6.1131 ◽

1993 ◽

Vol 91 (6) ◽

pp. 1131-1136 ◽

Cited By ~ 1

Author(s):

Ivan B. Pless ◽

Christine Power ◽

Catherine S. Peckham

Keyword(s):

Relative Risks ◽

Psychological Disturbances ◽

Psychosocial Sequelae ◽

Social Drinking ◽

Chronic Disorder ◽

Physical Disorders ◽

Chronic Disorders ◽

The Impact

Objective. This study was designed to examine the long-term psychosocial sequelae of chronic physical disorders that begin during childhood. Design. We analyzed data from a national birth cohort. 12 537 children were followed until age 23 years—76% of all born in Britain during one week in 1958. Of these, 1667 had a chronic disorder before age 16 and 1279 were included in the 23-year follow-up. Measures. Outcome measures included self-reported psychological disturbances between ages 16 and 23, scores on the Malaise Inventory, social class, educational qualifications, unemployment, and social activities. Results. The total cumulative incidence rate before 16 years was 109.5 per 1000. Demographic comparisons showed that the group with chronic physical disorders was similar to those free of chronic disorders in all respects except the sex ratio. Men with chronic physical disorders had significantly higher relative risks for abnormal scores on the Malaise Inventory (1.52, confidence interval [C]]1.13, 2.05); specialist psychological care (1.43, CI 1.00, 2.03); poor educational qualifications (1.26, CI 1.08, 1.47); periods of unemployment (1.20, CI 1.03, 1.41); and less social drinking (1.36, CI 1.15, 1.60). In contrast, women only had a significantly elevated risk for having seen a mental health specialist (1.32, CI 1.02, 1.71). Among the men some of the risks were further elevated for those in specific diagnostic groups. These findings are examined in the light of postulates about the impact of chronic physical disorders as a whole and in an attempt to explain the striking sex differences. For clinicians they provide further reason to justify concern about the psychosocial aspects of care for children with chronic disorders.

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Methods to determine the impact of rainfall on fuels and burned area in southern African savannas

International Journal of Wildland Fire ◽

10.1071/wf08207 ◽

2010 ◽

Vol 19 (6) ◽

pp. 774 ◽

Cited By ~ 7

Author(s):

S. Archibald ◽

A. Nickless ◽

R. J. Scholes ◽

R. Schulze

Keyword(s):

Southern Africa ◽

Linear Models ◽

Burned Area ◽

Rainfall Index ◽

Occurrence Data ◽

Accumulated Rainfall ◽

Input Variables ◽

The Impact ◽

The Relationship

In southern African savannas, grass production, and therefore the annual extent of fire, is highly dependent on rainfall. This response has repeatedly been noted in the literature but authors used different input variables and modelling approaches and the results are not comparable. Using long-term fire occurrence data from six protected areas in southern Africa we tested various methods for determining the relationship between antecedent rainfall and burned area. The types of regression model, the most appropriate index of accumulated rainfall, and the period over which to calculate annual burned area were all investigated. The importance of accumulating rainfall over more than one growing season was verified in all parks – improving the accuracy of the models by up to 30% compared with indices that only used the previous year’s rainfall. Up to 56% of the variance in burned area between years could be explained by an 18-month accumulated rainfall index. Linear models and probit models performed equally well. The method suggested in this paper can be applied across southern Africa. This will improve our understanding of the drivers of interannual variation in burned area in this globally important fire region.

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APIFLAME v1.0: high resolution fire emission model and application to the Euro-Mediterranean region

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-5489-2013 ◽

2013 ◽

Vol 6 (4) ◽

pp. 5489-5551

Author(s):

S. Turquety ◽

L. Menut ◽

B. Bessagnet ◽

A. Anav ◽

N. Viovy ◽

...

Keyword(s):

High Resolution ◽

Mediterranean Area ◽

Emission Factors ◽

Fuel Load ◽

Burned Area ◽

Emission Model ◽

Fire Emissions ◽

The Mediterranean ◽

The Impact ◽

Horizontal Grid

Abstract. This paper describes a new model for the calculation of daily, high-resolution (up to 1 km) fire emissions, developed in the framework of the APIFLAME project (Analysis and Prediction of the Impact of Fires on Air quality ModEling). The methodology relies on the classical approach, multiplying the burned area by the fuel load and the emission factors specific to the vegetation burned. Emissions can be calculated on any user-specified domain, horizontal grid, and list of trace gases and aerosols, providing input information on the burned area (location, extent) and emission factors of the targeted species are available. The strength of the proposed algorithm is its high resolution and its flexibility in terms of domain and input data (including the vegetation classification). The modification of the default values and databases proposed does not require changes in the core of the model. The code may be used for the calculation of global or regional inventories. However, it has been developed and tested more specifically for Europe and the Mediterranean area. In this region, the burning season extends from June to October in most regions, with generally small but frequent fires in Eastern Europe, Western Russia, Ukraine and Turkey, and large events in the Mediterranean area. The resulting emissions represents a significant fraction of the total yearly emissions (on average amounting to ~30% of anthropogenic emissions for PM2.5, ~20% for CO). The uncertainty on the daily carbon emissions was estimated to ~100% based on an ensemble analysis. Considering the large uncertainties on emission factors, the potential error on the emissions for the various pollutants is even larger. Comparisons to other widely used emission inventories shows good correlations but discrepancies of a factor of 2–4 on the amplitude of the emissions, our results being generally on the higher end.

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APIFLAME v1.0: high-resolution fire emission model and application to the Euro-Mediterranean region

Geoscientific Model Development ◽

10.5194/gmd-7-587-2014 ◽

2014 ◽

Vol 7 (2) ◽

pp. 587-612 ◽

Cited By ~ 48

Author(s):

S. Turquety ◽

L. Menut ◽

B. Bessagnet ◽

A. Anav ◽

N. Viovy ◽

...

Keyword(s):

High Resolution ◽

Regional Analysis ◽

Mediterranean Area ◽

Emission Factors ◽

Fuel Load ◽

Burned Area ◽

Emission Model ◽

Fire Emissions ◽

The Mediterranean ◽

The Impact

Abstract. This paper describes a new model for the calculation of daily, high-resolution (up to 1 km) fire emissions, developed in the framework of the APIFLAME (Analysis and Prediction of the Impact of Fires on Air quality ModEling) project. The methodology relies on the classical approach, multiplying the burned area by the fuel load consumed and the emission factors specific to the vegetation burned. Emissions can be calculated on any user-specified domain, horizontal grid, and list of trace gases and aerosols, providing input information on the burned area (location, extent), and emission factors of the targeted species are available. The applicability to high spatial resolutions and the flexibility to different input data (including vegetation classifications) and domains are the main strength of the proposed algorithm. The modification of the default values and databases proposed does not require any change in the core of the model. The code may be used for the calculation of global or regional inventories. However, it has been developed and tested more specifically for Europe and the Mediterranean area. A regional analysis of fire activity and the resulting emissions in this region is provided. The burning season extends from June to October in most regions, with generally small but frequent fires in eastern Europe, western Russia, Ukraine and Turkey, and large events in the Mediterranean area. The resulting emissions represent a significant fraction of the total yearly emissions (on average amounting to ~ 30% of anthropogenic emissions for PM2.5, ~ 20% for CO). The uncertainty regarding the daily carbon emissions is estimated at ~ 100% based on an ensemble analysis. Considering the large uncertainties regarding emission factors, the potential error on the emissions for the various pollutants is even larger. Comparisons with other widely used emission inventories show good correlations but discrepancies of a factor of 2–4 in the amplitude of the emissions, our results being generally on the higher end.

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Assessing variability and long-term trends in burned area by merging multiple satellite fire products

Biogeosciences ◽

10.5194/bg-7-1171-2010 ◽

2010 ◽

Vol 7 (3) ◽

pp. 1171-1186 ◽

Cited By ~ 459

Author(s):

L. Giglio ◽

J. T. Randerson ◽

G. R. van der Werf ◽

P. S. Kasibhatla ◽

G. J. Collatz ◽

...

Keyword(s):

Tropical Rainfall Measuring Mission ◽

Primary Data ◽

Burned Area ◽

Local Regression ◽

Data Set ◽

Fire Emissions ◽

Area Burned ◽

Time Period ◽

Long Term Trends

Abstract. Long term, high quality estimates of burned area are needed for improving both prognostic and diagnostic fire emissions models and for assessing feedbacks between fire and the climate system. We developed global, monthly burned area estimates aggregated to 0.5° spatial resolution for the time period July 1996 through mid-2009 using four satellite data sets. From 2001–2009, our primary data source was 500-m burned area maps produced using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance imagery; more than 90% of the global area burned during this time period was mapped in this fashion. During times when the 500-m MODIS data were not available, we used a combination of local regression and regional regression trees developed over periods when burned area and Terra MODIS active fire data were available to indirectly estimate burned area. Cross-calibration with fire observations from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) and the Along-Track Scanning Radiometer (ATSR) allowed the data set to be extended prior to the MODIS era. With our data set we estimated that the global annual area burned for the years 1997–2008 varied between 330 and 431 Mha, with the maximum occurring in 1998. We compared our data set to the recent GFED2, L3JRC, GLOBCARBON, and MODIS MCD45A1 global burned area products and found substantial differences in many regions. Lastly, we assessed the interannual variability and long-term trends in global burned area over the past 13 years. This burned area time series serves as the basis for the third version of the Global Fire Emissions Database (GFED3) estimates of trace gas and aerosol emissions.

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Modelling biomass burning emissions and the effect of spatial resolution: a case study for Africa based on the Global Fire Emissions Database (GFED)

Geoscientific Model Development ◽

10.5194/gmd-12-4681-2019 ◽

2019 ◽

Vol 12 (11) ◽

pp. 4681-4703

Author(s):

Dave van Wees ◽

Guido R. van der Werf

Keyword(s):

Spatial Resolution ◽

Input Data ◽

Large Scale ◽

Sub Saharan Africa ◽

Fuel Load ◽

Burned Area ◽

Fire Emissions ◽

Continental Scale ◽

Coarse Resolution ◽

The Impact

Abstract. Large-scale fire emission estimates may be influenced by the spatial resolution of the model and input datasets used. Especially in areas with relatively heterogeneous land cover, a coarse model resolution might lead to substantial errors in estimates. We developed a model using MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations of burned area and vegetation characteristics to study the impact of spatial resolution on modelled fire emission estimates. We estimated fire emissions for sub-Saharan Africa at 500 m spatial resolution (native MODIS burned area) for the 2002–2017 period, using a simplified version of the Global Fire Emissions Database (GFED) modelling framework, and compared this to model runs at a range of coarser resolutions (0.050, 0.125, 0.250∘). We estimated fire emissions of 0.68 Pg C yr−1 at 500 m resolution and 0.82 Pg C yr−1 at 0.25∘ resolution; a difference of 24 %. At 0.25∘ resolution, our model results were relatively similar to GFED4, which also runs at 0.25∘ resolution, whereas our 500 m estimates were substantially lower. We found that lower emissions at finer resolutions are mainly the result of reduced representation errors when comparing modelled estimates of fuel load and consumption to field measurements, as part of the model calibration. Additional errors stem from the model simulation at coarse resolution and lead to an additional 0.02 Pg C yr−1 difference in estimates. These errors exist due to the aggregation of quantitative and qualitative model input data; the average- or majority- aggregated values are propagated in the coarse-resolution simulation and affect the model parameterization and the final result. We identified at least three error mechanisms responsible for the differences in estimates between 500 m and 0.25∘ resolution simulations, besides those stemming from representation errors in the calibration process, namely (1) biome misclassification leading to errors in parameterization, (2) errors due to the averaging of input data and the associated reduction in variability, and (3) a temporal mechanism related to the aggregation of burned area in particular. Even though these mechanisms largely neutralized each other and only modestly affect estimates at a continental scale, they lead to substantial error at regional scales with deviations of up to a factor 4 and may affect large-scale estimates differently for other continents. These findings could prove valuable in improving coarse-resolution models and suggest the need for increased spatial resolution in global fire emission models.

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