Development of a Coupled Spatiotemporal Algal Bloom Model for Coastal Areas: A Remote Sensing and Data Mining-Based Approach

Elkadiri, R.; Manche, Cameron J.; Sultan, M.; Al-Dousari, Ahmad; Uddin, Saif; Chouinard, K.; Abotalib, Abotalib Z.

doi:10.1109/jstars.2016.2555898

Cited by 36 publications

(29 citation statements)

References 47 publications

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“…Globally, networks of air quality monitoring stations can be utilized on the local, national, and worldwide scales (Elkadiri et al 2016 ), due to the cumulative observations of the collected data on long-term periods: (a) a full understanding of dominant trends of pollution on the studied sites, (b) evaluation of the abrupt episodes of air pollution, (c) informing the public by the levels of local air quality, and (d) providing the decision-makers and policymakers with the accumulated levels of air quality to optimize the most compatible strategies for controlling the pollution levels (Cyrys et al 2012 ).…”

Section: Methodsmentioning

confidence: 99%

“…This research presents a general overview and the major trend of ambient air pollution and mapping the spatiotemporal variability of air pollutants in Makkah city, using a continuous air quality monitoring network (6 sites) during the pre-pandemic (January 1 to March 10, 2020) and during the COVID-19 pandemic lockdown (March 11 to August 31, 2020), throughout the major air quality indicators (SO 2 , NO 2 , CO, O 3 , and PM 10 ) with meteorological parameters (wind speed, wind direction, relative humidity, temperature, and rainfall), in addition to the average rate of decrease of ambient air quality parameters in Makkah resulting from COVID-19 lockdown. Globally, networks of air quality monitoring stations can be utilized on the local, national, and worldwide scales (Elkadiri et al 2016), due to the cumulative observations of the collected data on long-term periods: (a) a full understanding of dominant trends of pollution on the studied sites, (b) evaluation of the abrupt episodes of air pollution, (c) informing the public by the levels of local air quality, and (d) providing the decision-makers and policymakers with the accumulated levels of air quality to optimize the most compatible strategies for controlling the pollution levels (Cyrys et al 2012).…”

Section: Responsible Editor: Amjad Kallelmentioning

confidence: 99%

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Assessing the air quality of megacities during the COVID-19 pandemic lockdown: a case study from Makkah City, Saudi Arabia

2021

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Many countries are concerned about how concentrations of outdoor air pollutants have decreased due to control measures aftermath of the COVID-19 outbreak. In Saudi Arabia, the measures started with suspending entry to Saudi Arabia on February 27, 2020, and ended with full prevention of Saudis for outdoor movement on April 2, 2020, until the back return to normal life by the start of September 2020. These measures are expected to impact air quality levels during this period by decreasing the concentrations of air pollutants. This paper investigates the major trends of ambient air pollution (SO 2 , NO 2 , CO, O 3 , and PM 10), and maps the spatiotemporal variability of air pollutants in Makkah city, using a continuous air quality monitoring network (6 sites) during the pre-pandemic period. Findings indicate the presence of a significant decrease of concentration rates during the lockdown period, compared with the pre-pandemic period, by 26.34% for SO 2 , 28.99% for NO 2 , 26.24% for CO, 11.62% for O 3 , and 30.03% for PM 10. Therefore, the vehicular traffic activities represent 25-30% of the total air pollution load in Makkah city. The COVID-19 lockdown conditions in Makkah provide a unique opportunity to evaluate the impact of the reduction of vehicular traffic activities on the air quality levels of Makkah city and similar mega-cities and introduce credible solutions to improve the conditions of the ambient environment and air quality in such settings.

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Section: Methodsmentioning

confidence: 99%

Section: Responsible Editor: Amjad Kallelmentioning

confidence: 99%

Assessing the air quality of megacities during the COVID-19 pandemic lockdown: a case study from Makkah City, Saudi Arabia

2021

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“…SST influences phytoplankton productivity in multiple ways: (i) individual biological species including algal blooms thrive under different and specific temperature regimes, and (ii) the availability and solubility of many biochemical materials needed for their growth and development is temperature dependent [54,84]. Many studies have shown a correlation between SST and algal bloom distributions in the Mediterranean Basin [85], Kuwait Bay [86,87] and on a global scale [88]. The productivity of K. brevis increases in the fall and early spring at the west Florida shelf primarily because of the ideal temperature conditions during these times [19].…”

Section: Particulate Backscattering Coefficient At 547 Nmmentioning

confidence: 99%

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

et al. 2018

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Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

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“…Sea Surface Temperature (°C) SST influences phytoplankton productivity in multiple ways: (1) individual biological species including algal blooms thrive under different and specific temperature regimes, and (2) the availability and solubility of many biochemical materials needed for their growth and development is temperature dependent [65,35]. Many studies have shown a correlation between SST and algal bloom distributions in the Mediterranean Basin [66], Kuwait Bay [67,68] and on a global scale [69]. The productivity of K. brevis increases in the fall and early spring at the west Florida shelf primarily because of the ideal temperature conditions during these time periods [19].…”

Section: 17mentioning

confidence: 99%

Mapping and forecasting onsets of harmful algal blooms using MODIS data over coastal waters surrounding Charlotte County, Florida

Karki¹,

Sultan²,

Elkadiri³

et al. 2018

Preprint

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Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the reported historical events. The models were designed to specifically capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web-based GIS (http://www.esrs.wmich.edu/webmap/bloom/). Our findings include the following: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, SST, chlorophyll-a [OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable, dependent on readily available remote sensing data sets, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

show abstract

Development of a Coupled Spatiotemporal Algal Bloom Model for Coastal Areas: A Remote Sensing and Data Mining-Based Approach

Cited by 36 publications

References 47 publications

Assessing the air quality of megacities during the COVID-19 pandemic lockdown: a case study from Makkah City, Saudi Arabia

Assessing the air quality of megacities during the COVID-19 pandemic lockdown: a case study from Makkah City, Saudi Arabia

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

Mapping and forecasting onsets of harmful algal blooms using MODIS data over coastal waters surrounding Charlotte County, Florida

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