Fire Count Data Research Articles

A Bayesian model for predicting monthly fire frequency in Kenya.

This study presents a comprehensive analysis of historical fire and climatic data to estimate the monthly frequency of vegetation fires in Kenya. This work introduces a statistical model that captures the behavior of fire count data, incorporating temporal explanatory factors and emphasizing the predictive significance of maximum temperature and rainfall. By employing Bayesian approaches, the paper integrates literature information, simulation studies, and real-world data to enhance model performance and generate more precise prediction intervals that encompass actual fire counts. To forecast monthly fire occurrences aggregated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in Kenya (2000-2018), the study utilizes maximum temperature and rainfall values derived from global GeoTiff (.tif) files sourced from the WorldClim database. The evaluation of the widely used Negative Binomial (NB) model and the proposed Bayesian Negative Binomial (BNB) model reveals the superiority of the latter in accounting for seasonal patterns and long-term trends. The simulation results demonstrate that the BNB model outperforms the NB model in terms of Root Mean Square Error (RMSE), and Mean Absolute Scaled Error (MASE) on both training and testing datasets. Furthermore, when applied to real data, the Bayesian Negative Binomial model exhibits better performance on the test dataset, showcasing lower RMSE (163.22 vs. 166.67), lower MASE (1.12 vs. 1.15), and reduced bias (-2.52% vs. -2.62%) compared to the NB model. The Bayesian model also offers prediction intervals that closely align with actual predictions, indicating its flexibility in forecasting the frequency of monthly fires. These findings underscore the importance of leveraging past data to forecast the future behavior of the fire regime, thus providing valuable insights for fire control strategies in Kenya. By integrating climatic factors and employing Bayesian modeling techniques, the study contributes to the understanding and prediction of vegetation fires, ultimately supporting proactive measures in mitigating their impact.

The prevailing smog conditions over the Delhi-NCR during the 2022 post monsoon

New Delhi experienced worst air-quality days and occurrence of severe smog on 03–04 November 2022. Remote sensing data from various platforms, NOAA HYSPLIT and ERA-5 is utilized to monitor the episode of high smog conditions over Delhi during October- November 2022. It has been observed that AOD has increased up to 4 times till 04 November 2022 when compared to before the starting of burning period. Since the anthropogenic source regions are concentrated around Delhi; the adjoining areas have shown increased burning using fire count data from MODIS Aqua. Study revealed that wind pattern with reduced speeds at lower altitudes is favored to sustain smog for an extended period. The study revealed that the smog over Delhi region for an extended period is formed majorly by the transport of particulate matter and CO emission from nearby areas, compounded by the prevailing fog. Air-mass trajectory analysis confirms that atmosphere over Delhi region was influenced by the transported mass of aerosol particles which corroborated well with the areas characterized by the fire counts, indicating a role of residual burning.

Light-absorbing properties of polar- and non-polar brown carbon fractions of aerosols in Delhi

In the present study, PM2.5 samples (n = 104) were collected during February 2020–March 2021 on diurnal basis in Delhi (a site of Indo-Gangetic Plain (IGP)), with a view to investigate seasonal and diurnal patterns of ambient brown carbon (BrC) with consideration of their potential sources. Given that the polarity of ambient aerosol influences their chemical and physical processing in the atmosphere, we compared the light absorption properties of brown carbon (BrC) in PM2.5 samples extracted with three different solvents i.e., water, methanol, and n-hexane. Methanol extracts showed much stronger light-absorbing ability followed by water and hexane extracts in the wavelength range of 300–600 nm. Peak values of mass absorption coefficient (babs-BrC-365nm) were observed during post-monsoon season in both polar- (water-and methanol-soluble) and non-polar (n-hexane soluble) fractions of BrC, mainly due to the influence of biomass-burning (BB) aerosols. This point is further supported by air mass back trajectories which are well correlated with fire-count data, and marker species such as EC, OC and non-sea-salt K+. Comparison of babs-BrC-365nm, mass absorption efficiency (MAE365nm), absorption Ångstorm exponent (AÅE) and simple forcing efficiency (SFE300–700) from this study with that in published datasets reveal the significant contribution of biomass-burning activities to BrC absorption and abundance in the ambient atmosphere. Furthermore, the optical properties of HULIS (humic-like substances) (n = 40, during day- and nighttime) in post-monsoon (October – November 2020) and winter aerosols (January – February 2021) were also characterised. Based on UV–Vis analysis, similar average babs-365nm of HULIS was observed for daytime and nighttime samples i.e., 9.96 ± 3.8 and 9.89 ± 3.3 Mm−1, respectively in post-monsoon samples. Opposingly in winter period, significantly high daytime value of babs-365nm of HULIS (7.24 ± 2.7 M m−1) compared to that of nighttime (4.45 ± 2.8 M m−1) was found. These results along with indicative E2/E3 (ratio of absorbance at 250 nm and 365 nm, respectively) and AÅE values in HULIS fraction suggest a more significant contribution from regional crop-residue burning emission in the formation of HULIS in post-monsoon. While in winter period local emissions and subsequent secondary formation of HULIS are important. To the best of our knowledge, this study is the first attempt to investigate light absorption properties of different polarity BrC extracts (methanol, water, n-hexane, HULIS) in aerosol samples collected throughout the year at a representative site in Indo-Gangetic Plain (IGP).

Stubble-burning activities in north-western India in 2021: Contribution to air pollution in Delhi

Stubble-burning in northern India is an important source of atmospheric particulate matter (PM) and trace gases, which significantly impact local and regional climate, in addition to causing severe health risks. Scientific research on assessing the impact of these burnings on the air quality over Delhi is still relatively sparse. The present study analyzes the satellite-retrieved stubble-burning activities in the year 2021, using the MODIS active fire count data for Punjab and Haryana, and assesses the contribution of CO and PM2.5 from such biomass-burning activities to the pollution load in Delhi. The analysis suggests that the satellite-retrieved fire counts in Punjab and Haryana were the highest among the last five years (2016–2021). Further, we note that the stubble-burning fires in the year 2021 are delayed by ∼1 week compared to that in the year 2016. To quantify the contribution of the fires to the air pollution in Delhi, we use tagged tracers for CO and PM2.5 emissions from fire emissions in the regional air quality forecasting system. The modeling framework suggests a maximum daily mean contribution of the stubble-burning fires to the air pollution in Delhi in the months of October–November 2021 to be around 30–35%. We find that the contribution from stubble burning activities to the air quality in Delhi is maximum (minimum) during the turbulent hours of late morning to afternoon (calmer hours of evening to early morning). The quantification of this contribution is critical from the crop-residue and air-quality management perspective for policymakers in the source and the receptors regions, respectively.

Investigation of sources and atmospheric transformation of carbonaceous aerosols from Shyamnagar, eastern Indo-Gangetic Plains: Insights from δ13C and carbon fractions

This study reports the chemical characterization of the carbonaceous component of PM2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) collected over a year-long campaign from a regional site in Shyamnagar, West Bengal, in the Indo-Gangetic Plains (IGP), India. The carbonaceous fractions (elemental and organic carbon), mass concentrations, and stable carbon isotopic composition (δ13C value) of aerosols were measured and utilized to characterize the sources and understand the atmospheric processing of aerosols. Cluster analysis, Potential Source Contribution Function (PSCF) modeling, and fire count data were analyzed to decipher the pattern of air masses, source contributions, and extent of burning activities. The PM2.5 mass concentrations were significantly higher during winter (168.3 ± 56.3 μg m−3) and post-monsoon (109.8 ± 59.1 μg m−3) compared to the monsoon (29.8 ± 10.7 μg m−3) and pre-monsoon (55.1 ± 23.0 μg m−3). Organic carbon (OC), elemental carbon (EC), and total carbon (TC) concentrations were also several factors higher during winter and post-monsoon compared to monsoon and pre-monsoon. The winter and post-monsoon experienced the impact of air masses from upwind IGP. On the other hand, long-range transported air masses from the South-West direction dominated during monsoon and pre-monsoon, which are also relatively cleaner periods. The average δ13C during post-monsoon and winter was ∼1‰ higher compared to monsoon and pre-monsoon. The vehicular exhaust and biomass/biofuel burning contributed dominantly in winter and post-monsoon. In comparison, lower δ13C in pre-monsoon and monsoon might be attributed to the dominance of biomass/biofuel combustion. Photochemical-induced aging of the anthropogenic aerosols resulted in a higher δ13C of TC in winter and post-monsoon, whereas the mixing of different local sources in pre-monsoon and monsoon resulted in lower δ13C values. These findings benefit policymakers in strategizing proper and effective management of biomass/biofuel burning in the IGP to minimize air pollution.

Increasing Wind Speeds Fuel the Wider Spreading of Pollution Caused by Fires over the IGP Region during the Indian Post-Monsoon Season

Every year, forest fires and harvest harnessing produce atmospheric pollution in October and November over the Indo-Gangetic Plain (IGP). The fire count data (MODIS) shows a decreasing/increasing trend of fire counts in all confidence ranges in October/November over Northern India. There is a widespread increase in fires with a confidence level above 60 to 80% over the whole Northern Indian region. The Aerosol Optical Index (AOD) also shows an increase with values > 0.7 over the northwestern and IGP regions. There have been some startling results over the lower IGP belt, where there has been increasing trend in AOD during October ~56% and during November, the increase was by a whopping ~116%. However, in November, a slight turning of the winds towards central India might be transporting the AOD towards the central Indian region. Hence, during November, it is inferred that due to the low wind speed over the lower IGP belt and increased fires, the AODs in the polluted air tend to hover for a long time. During recent years from 2010, the winds have become stronger, indicating more transport of AOD is occurring over the lower IGP belt as compared to previous years till 2009, especially in October.

Insights into the Regional Transport and Local Formation of Secondary Organic Aerosol in Delhi, India

The organic carbon (OC) and elemental carbon (EC) content were measured from PM2.5 quartz filter substrates collected over 24 hours in Faridabad, a heavily polluted city in Delhi national capital region (NCR), during January–February 2018. In this study, the secondary fraction of OC (SOC) was calculated using the ‘EC-tracer method’, and subsequently, secondary organic aerosol (SOA) was calculated. Some typical episodes were observed where SOA was elevated for multiple days. These occurrences were investigated for aged SOA formation through multi-day regional transport and locally generated SOA using ozone (O3), carbon monoxide (CO), nitric oxide (NO), and volatile organic compounds (VOCs) as supplementary data. Higher OC/EC ratios during these episodes could be due to a strong primary source, and this possibility cannot be ruled out. However, similar sulfate dynamics with SOA consolidated the multi-day regional transport of SOA during the elevated SOA episodes. The moderate resolution imaging spectroradiometer (MODIS) fire count and planetary boundary layer height (PBLH) data were examined to strengthen the hypothesis that the elevated SOA days were due to multi-day regional transport of SOA and not locally formed. The ratios of volatile organic compounds (VOC) were analyzed to understand the origins of VOCs and also to explore the regional transformation and local formation of SOA. The toluene/benzene (T/B) and isoprene/benzene (I/B) ratios were observed during the multi-day elevated SOA episodes to distinguish the regional and local sources of VOCs. Concentration weighted trajectory (CWT) plots also confirmed our hypothesis. This study suggests that the higher SOC fraction during those episodes was most likely due to regional or aged SOA formation. Also, multi-day regional transport contributes to the higher and elevated SOA episodes during January–February 2018.

Temporal variability of atmospheric particulate-bound polycyclic aromatic hydrocarbons (PAHs) over central east India: sources and carcinogenic risk assessment.

Atmospheric polycyclic aromatic hydrocarbons (PAHs) are of significant interest owing to their high potential health effects, including mutagenicity and carcinogenicity. We report 16 PAHs measured in ambient PM2.5 from June 2018 to May 2019 over three different sites located in central east India. The annual average PM2.5 mass concentrations of 97.3 ± 18.1 µg m−3, 101.9 ± 19.4 µg m−3, and 93.9 ± 20.3 µg m−3 were measured at RCI (Ranchi), GHY (Gamharia), and BKR (Bokaro), respectively. The mass concentrations at all sampling sites are relatively higher than the annual average concentration of the National Ambient Air Quality Standard. Total annual PAH concentrations (ng m−3) are found to be comparable at BKR (797.9 ± 39.1 ng m−3) and RCI (887.7 ± 38.8 ng m−3); however, a relatively higher average is observed over GHY (1015.1 ± 42.7 ng m−3). Using PAH diagnostic ratios and principal component analysis (PCA), their major sources were attributed to coal and wood combustion as well as vehicular emission of diesel and gasoline at all sampling sites. Significant seasonal variability is observed for PAH composition and mainly attributed to change in emission sources. Summer and winter compositions were found to be impacted by the transport from Indo-Gangetic Plains (IGP). However, ambient level PAHs during the post-monsoon season were impacted by mixed sources from Indo-Gangetic Plain and eastern India. These observations are supported by the analysis of back-trajectory and fire count data. The excess life time cancer risk (ELCR) values estimated for the study sites are within acceptable limits suggesting acceptable risk levels at BKR, GHY, and RCI. This study highlights the significance of ambient aerosol concentration for health risks in the pre-COVID-19 scenario.Graphical

Local emission and long-range transport impacts on the CO, CO2, and CH4 concentrations at a tropical rural site

Local emission and long-range transport impacts on the observed concentrations (dry air mole fractions) of CO, CO2, and CH4 have been investigated using three years (April 2016 to April 2019) of surface measurements at a tropical rural site, Gadanki (13.5° N, 79.2° E) in Southern peninsular India. Gadanki represents the poor coverage area of Greenhouse Gas (GHG) measurements in India and has both local emission and long-range transport influences on its air masses. All the three gases have shown strong seasonality and diurnal variations. Trend analysis showed a significant decreasing trend in CO (14.3 ± 0.2 ppbv/year) and increasing trends in CO2 (2.5 ± 1.2 ppmv/year) and CH4 (11.1 ± 0.03 ppbv/year). CO2 and CH4 trends are marginally higher than the global trends (2.2 ± 0.004 ppmv/year and 7.0 ± 0.001 ppbv/year, respectively) for the same period highlighting the sustained local emission impact on the observed concentrations. Among the three species, CO has been the most local emission impacted species with local emission contribution (to the total observed concentration) varying between 4 and 25% during different seasons with a median value always lying above 10% irrespective of the season. In the case of CO2 and CH4, the local emission contribution found to vary in the range of 0.8–7% and 0.15–3%, respectively. Long-range transport impact dominance over the local emission is observed in CO2 and CH4 during all the seasons. Analysis using MODIS fire count data, Concentration Weighted Trajectory (CWT) and FLEXPART have reconfirmed the combined effect of local and long-range transport impacts on the observed concentrations. Comparison of the IASI MetOp and AIRS satellite data products with surface measurements showed the significant bias and poor representation of the seasonality demonstrating the limitations on the sensitivity of satellite trace gas measurements within the boundary layer.

Comparing four regression techniques to explore factors governing the number of forest fires in Southeast, China

Four regression techniques, including two global models (i.e., Poisson and negative binominal) and two geographically weighted regression (GWR) models (i.e., geographically weighted Poisson regression (GWPR) and geographically weighted negative binominal regression (GWNBR)) were used to explore which was the most suitable method for predicting the number of forest fires and to investigate the spatially varying relationships between forest fires and environmental factors in Fujian province, in the Southeast of China. Our results showed that the GWR models fitted the fire count data better than the global models, and yielded more realistic spatial distributions of model predictions. Particularly, GWNBR was superior for addressing overdispersion in the fire count data because it estimated the dispersion parameter at a local level. Additionally, our study indicated that more forest fires occurred in areas of lower elevation, flatter terrain, and higher population density. The global models showed that precipitation had positive impacts on fire occurrence in the study area. In contrast, the GWR models revealed that precipitation was positively related to the forest fires in the western regions of Fujian, but negatively related in the eastern coastal regions. Our study could provide better insight into forest fire management based on local environmental characteristics.

An Investigation of the Elevated Aerosol Layer Using a Polarization Lidar Over a Tropical Rural Site in India

Polarization lidar observations were made to study the transport of an elevated aerosol layer over Gadanki, India (13.45° N, 79.17° E) during the pre-monsoon period of the year 2009. Observations show significant aerosol layering within and above the boundary layer. Coordinated observations with radiosondes were carried out from 2 to 10 April 2009. Temporal and spatial variations of the parameters are studied for the boundary layer (≈ 2.5 km) and up to 5 km. The backscattering coefficient and the depolarization ratio are observed to increase and decrease with an increase in humidity, respectively. Clouds are not formed, indicating less efficiency of the aerosol in acting as condensation nuclei. The transport of the elevated aerosol layer is investigated using a back-trajectory analysis, revealing that the transported layer originating from the central Indian region has a depolarization ratio of at least 0.05. From model analysis and satellite fire-count data, it is inferred that the source of the aerosol layer is wildfire events over the central Indian region. The elevated smoke-aerosol layer (not mixing with the boundary layer) has implications for the altering of the temperature profile of the atmosphere and the suppression of cloud formation.

Investigation of size distribution and mass characteristics of ambient aerosols and their combustion sources during post-monsoon in northern India

This study explores the size distribution and mass characteristics of ambient atmosphere in the source region of biomass burning (Punjab). The enhanced level of particulate matter (PM) concentrations were interpreted through measurements of number and mass concentration. MODIS (Moderate Resolution Imaging Spectroradiometer) fire count data and HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory model) used to decipher the extent of burning activities and origin of air-masses, respectively. Significant diurnal variabilities were observed in the PM1 (PM with aerodynamic diameter ≤ 1 μm) and PM2.5 (aerodynamic diameter ≤ 2.5 μm) number and mass concentrations. Higher number (N0.3–2.5: 1.34 ± 0.20 × 104 #/cm3) and mass concentration (PM2.5: 286 ± 39 μg/m3, n = 5) during nighttime compared to daytime (N2.5: 0.74 ± 0.22 × 104 #/cm3; PM2.5: 224 ± 59 μg/m3, n = 3) indicates impact of meteorological conditions (low wind speed and shallower boundary layer height) and biomass (plausibly paddy) burning activity. Although possibility of multiple sources and their varying strength (poor correlation between organic carbon (OC) and elemental carbon (EC)) can't be neglected, however, biomass burning appeared to be the dominant source (good correlation (R2 = 0.4) between water-soluble potassium (K+) and PM) of ambient PM. Further, near similar number and mass size distribution of ambient and bio-fuels derived aerosols along with similar K+/PM and OC/EC ratio compared to that for paddy-residue burning imply that bio-fuel burning in the household kitchen could be the dominant contributor to enhanced PM concentration at sampling location in upwind IGP during the study period.

Carbon isotope‐constrained seasonality of carbonaceous aerosol sources from an urban location (Kanpur) in the Indo‐Gangetic Plain

AbstractThe Indo‐Gangetic Plain (IGP) in northern India, Pakistan, and Bangladesh is a major source of carbonaceous aerosols in South Asia. However, poorly constrained seasonality of their sources over the IGP leads to large uncertainty in climate and health effects. Here we present a first data set for year‐round radiocarbon (Δ14C) and stable carbon (δ13C)‐based source apportionment of total carbon (TC) in ambient PM10 (n = 17) collected from an urban site (Kanpur: 26.5°N, 80.3°E) in the IGP during January 2007 to January 2008. The year‐round 14C‐based fraction biomass (fbio‐TC) estimate at Kanpur averages ~77 ± 7% and emphasizes an impact of biomass burning emissions (BBEs). The highest fbio‐TC (%) is observed in fall season (October–November, 85 ± 6%) followed by winter (December–February, 80 ± 4%) and spring (March–May, 75 ± 8%), while lowest values are found in summer (June–September, 69 ± 2%). Since biomass/coal combustion and vehicular emissions mostly contribute to carbonaceous aerosols over the IGP, we predict δ13CTC (δ13Cpred) over Kanpur using known δ13C source signatures and the measured Δ14C value of each sample. The seasonal variability of δ13Cobs − δ13Cpred versus Δ14CTC together with air mass back trajectories and Moderate Resolution Imaging Spectroradiometer fire count data reveal that carbonaceous aerosols in winter/fall are significantly influenced by atmospheric aging (downwind transport of crop residue burning/wood combustion emissions in the northern IGP), while local sources (wheat residue combustion/vehicular emissions) dominate in spring/summer. Given the large temporal and seasonal variability in sources and emission strength of TC over the IGP, 14C‐based constraints are, thus, crucial for reducing their uncertainties in carbonaceous aerosol budgets in climate models.

Modelling of fire count data: fire disaster risk in Ghana.

Stochastic dynamics involved in ecological count data require distribution fitting procedures to model and make informed judgments. The study provides empirical research, focused on the provision of an early warning system and a spatial graph that can detect societal fire risks. It offers an opportunity for communities, organizations, risk managers, actuaries and governments to be aware of, and understand fire risks, so that they will increase the direct tackling of the threats posed by fire. Statistical distribution fitting method that best helps identify the stochastic dynamics of fire count data is used. The aim is to provide a fire-prediction model and fire spatial graph for observed fire count data. An empirical probability distribution model is fitted to the fire count data and compared to the theoretical probability distribution of the stochastic process of fire count data. The distribution fitted to the fire frequency count data helps identify the class of models that are exhibited by the fire and provides time leading decisions. The research suggests that fire frequency and loss (fire fatalities) count data in Ghana are best modelled with a Negative Binomial Distribution. The spatial map of observed fire frequency and fatality measured over 5 years (2007–2011) offers in this study a first regional assessment of fire frequency and fire fatality in Ghana.

Regional biomass burning trends in India: Analysis of satellite fire data

The results based on the analysis of satellite fire counts detected by the Along-Track Scanning Radiometer (ATSR) sensors over different regions of India during 1998–2009 have been presented. Generally, the activities of open biomass burning show large spatial and temporal variations in India. The highest and lowest values of monthly fire counts were detected during the periods of March–May and July–September, respectively over different regions of India. The activities of biomass burning in two central states of Madhya Pradesh and Maharashtra were the highest and together accounted for about 25–45% of total annual fire counts detected over India during the study period. However, in opposite phases, the rainfall and fire count data show strong seasonal variation. In addition to large regional and seasonal variations, the fire data also show significant year-to-year variation. The higher annual fire counts exceeding the mean of entire period by about 16% and 43% were detected during the two periods of 1998–2000 and 2007–2009, respectively. We have estimated normalized anomaly of annual fire count data which shows large positive departures from long-term mean for the years 1999, 2007, 2008 and 2009, while negative departures for the years 2002, 2003 and 2005. Consistently, the mixing ratio of carbon monoxide (CO) typical peaks during winter but extended to pre-monsoon season during extensive fire years. The annual data over the entire region of India show lesser positive trend of about 3% yr−1. The inter-annual variation of fire count over entire India follows the trend in the ENSO Precipitation Index (ESPI) but shows opposite trend to the multivariate ENSO Index (MEI).

Improving satellite-driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S.

Multiple studies have developed surface PM2.5 (particle size less than 2.5 µm in aerodynamic diameter) prediction models using satellite-derived aerosol optical depth as the primary predictor and meteorological and land use variables as secondary variables. To our knowledge, satellite-retrieved fire information has not been used for PM2.5 concentration prediction in statistical models. Fire data could be a useful predictor since fires are significant contributors of PM2.5. In this paper, we examined whether remotely sensed fire count data could improve PM2.5 prediction accuracy in the southeastern U.S. in a spatial statistical model setting. A sensitivity analysis showed that when the radius of the buffer zone centered at each PM2.5 monitoring site reached 75 km, fire count data generally have the greatest predictive power of PM2.5 across the models considered. Cross validation (CV) generated an R2 of 0.69, a mean prediction error of 2.75 µg/m3, and root-mean-square prediction errors (RMSPEs) of 4.29 µg/m3, indicating a good fit between the dependent and predictor variables. A comparison showed that the prediction accuracy was improved more substantially from the nonfire model to the fire model at sites with higher fire counts. With increasing fire counts, CV RMSPE decreased by values up to 1.5 µg/m3, exhibiting a maximum improvement of 13.4% in prediction accuracy. Fire count data were shown to have better performance in southern Georgia and in the spring season due to higher fire occurrence. Our findings indicate that fire count data provide a measurable improvement in PM2.5 concentration estimation, especially in areas and seasons prone to fire events.

Spatial Temporal Analysis of Forest Fire in Malaysia Using ATSR Satellite Measurement

Over the last few decades, Malaysia experienced several severe forest fire episodes in history due to dry season and El-Niño events. In fact, research on fire event has become an important as its strongly responsive to climatic drivers, provide impacts on biogeochemical cycles and it tremendously affects ecosystems life. Therefore, this study aimed to collect and process Along Track Scanning Radiometer (ATSR) fire count data and to assess the distribution of fire occurrences in Malaysia. This study specifically used ATSR World Fire Atlas (WFA) product (Algorithm 2 data) from ATSR satellite data covering years 1997-2008. Preliminary results of this study show that there are 3 minor El-Niño events (June-December, July-November and September-October), and the large El-Niño event (January-April) had occurred as followed by an equally important La-Niña episode. Month of April and state of Sabah show the month and region with the highest fire events. There are several factors that can contribute to the forest fire events such as fire locations in Indonesia, anthropogenic activities, geographical and meteorological conditions and natural phenomenon such as lightning and El-Niño events. Findings from this study can be utilized to increase the understanding of spatial and temporal distribution of forest fire in Malaysia. ATSR Satellite measurements also found to be significantly helpful in providing a valuable fire data count.

Emissions of Volatile Organic Compounds from Biomass Burning Sources and their Ozone Formation Potential over India

Thousands of volatile organic compounds (VOCs) in the Earth's atmosphere exist which play an important role in various photochemical processes. However, the global model simulations of tropospheric chemistry deal with limited data of speciated VOCs. In the present study, we have used the Global Fire Emissions Database inventory of VOCs emitted from biomass burning in India during the period from 1997 to 2009. We have also analysed data of some VOCs measured in the upper troposphere over India for the year 2008. In this study, the major species analysed are C 2 H 4 , C 2 H 4 O, C 2 H 6 , C 2 H 6 S, C 3 H 6 , C 3 H 6 S, C 3 H 8 , C 5 H 8 , CH 3 OH, higher alkanes, higher alkenes, terpenes and toluene lumps. The biomass burning emissions of VOCs show large inter-annual variation. For example, the annual emission estimates of non-methane hydrocarbons (NMHCs) and CH3OH varied in the range 100-470 and 46-211 Gg yr -1 respectively. The major biomass sources were broadly categorized as deforestation, fuel-wood, forest and agricultural residues. The agricultural residue burning is the most dominant among the several biomass burning sources contributing to the emissions of CH 3 OH (59%), isoprene (80%) and toluene (72%). On the other hand, the major sources for NMHCs emission were agricultural residues and deforestation during all the years. The fire count data detected using the satellite-based Along Track Scanning Radiometer have been used to directly refer to the seasonal and inter-annual variations of biomass burning activities. We have estimated the propylene-equivalent concentrations of different light NMHCs measured in the upper troposphere over India. Role of stratospheric intrusion in the distribution of NMHCs has been analysed using the potential vorticity data.

Seasonal and interannual variability of tropospheric ozone over an urban site in India: A study based on MOZAIC and CCM vertical profiles over Hyderabad

This study is based on the analysis of Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft (MOZAIC) data measured over Hyderabad, India during the years 2006–2008. Tropospheric profiles of O3 show clear seasonality with high and low values during the premonsoon and monsoon seasons, respectively. Analysis of back trajectory and fire count data indicates major roles for long-range transport and biomass burning in the seasonal variation of O3. Typically, lower levels of O3 in the monsoon season were due to the flow of marine air and negligible regional biomass burning, while higher levels in other seasons were due to transport of continental air. In the upper troposphere, relatively low levels of O3 during the monsoon and postmonsoon seasons were associated with deep convection. In the free troposphere, levels of O3 also show year-to-year variability as the values in the premonsoon of 2006 were higher by about 30 ppbv compared to 2008. The year-to-year variations were mainly due to transition from El Nino (2006) to La Nina (2008). The higher and lower levels of O3 were associated with strong and weak wind shears, respectively. Typically, vertical variations of O3 were anticorrelated with the lapse rate profile. The lower O3 levels were observed in the stable layers, but higher values in the midtroposphere were caused by long-range transport. In the PBL region, the mixing ratio of O3 shows strong dependencies on meteorological parameters. The Chemistry Climate Model (CCM2) reasonably reproduced the observed profiles of O3 except for the premonsoon season.

Characteristics of tropospheric ozone variability over an urban site in Southeast Asia: A study based on MOZAIC and MOZART vertical profiles

AbstractThe Measurement of Ozone and Water Vapor by Airbus In‐Service Aircraft (MOZAIC) profiles of O3 and CO were analyzed to study their variation in the troposphere over Bangkok. Mixing ratios of O3 and CO were enhanced in planetary boundary layer (PBL) being highest in winter followed by summer and wet seasons. The daytime profiles of O3 show higher values compared to nighttime observations in PBL region, but little differences were observed in the free troposphere. The decreasing mixing ratios of O3 in the lower and upper troposphere were associated with shallow and deep convections, respectively. Back trajectory and fire count data indicate that the seasonal variations in trace gases were caused mainly by the regional shift in long‐range transport and biomass‐burning patterns. In wet season, flow of oceanic air and negligible presence of local biomass burning resulted in lowest O3 and CO, while their high levels in dry season were due to extensive biomass burning and transport of continental air masses. The Model for Ozone and Related Chemical Tracers (MOZART) underestimated both O3 and CO in the PBL region but overestimated these in the free troposphere. Simulations of O3 and CO also show the daytime/nighttime differences but do not capture several key features observed in the vertical distributions. The observed and simulated values of O3 and CO during September–November 2006 were significantly higher than the same period of 2005. The year‐to‐year differences were mainly due to El Niño‐led extensive fires in Indonesia during 2006 but normal condition during 2005.