Spatial and Temporal Distribution of Biomass Open Burning Emissions in the Greater Mekong Subregion

Junpen, Agapol; Roemmontri, Jirataya; Boonman, Athipthep; Cheewaphongphan, Penwadee; Thảo, Phạm Thị Bích; Garivait, Savitri

doi:10.3390/cli8080090

Cited by 9 publications

(9 citation statements)

References 51 publications

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“…The GMS is a group of six countries in the northeast of Southeast Asia: Cambodia, the Lao People's Democratic Republic, Myanmar, Thailand, Vietnam and China [9]. Land transportation is the most popular transportation mode in the GMS.…”

Section: Literature Reviewmentioning

confidence: 99%

“…All 2 objectives have been minimized and subjected to 11 constraints, as follows. Constraint (3) stipulates that container g located at CLC i delivers to at most one pair of borders and end markets and only when container g is located at CLC i. Constraint (4) stipulates that container g delivers to end market l only when end market l is allowed to ship via border k. Constraint (5) stipulates that vegetable v is shipped from farmer j to container g located at CLC i only when farmers j deliver the goods to container g. Constraint (6) stipulates that farmers can ship to container g located at CLC i only when container g is available at CLC i. Constraint (7) stipulates that vegetable v is shipped from farmer j if it does not exceed its availability at farm j. Constraint (8) defines the amount of vegetable v packed in container g. Constraint (9) stipulates that vegetable v is be shipped to market l more than its demand. Constraint (10) stipulates that container g's vegetables do not exceed its capacity.…”

Section: Decision Variablesmentioning

confidence: 99%

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A Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)

et al. 2022

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In recent years, agriculture products have contributed to 28.75% of Thailand’s GDP. China, Vietnam, Myanmar, Cambodia, Laos and Vietnam are the main markets for agricultural products. The annual export volume exceeds 119,222 million THB. The majority of them are shipped over Thailand’s land borders to its neighbors. Small and medium-sized farmers make up more than 85% of those who produce agricultural items. Numerous scholars have studied the transportation methods used by the Greater Mekong Subregion (GMS) nations along the economic corridor, but the majority of them have concentrated on import–export operations involving sizable firms, which are not applicable to the transportation of agricultural products, particularly when attention is paid to small and medium-sized farmers. In this study, mixed-integer programming (MIP) is presented to design an agricultural product logistics network. In order to prolong the lifespan of the container used, the MIP’s primary goal is to maximize the total chain profit while maintaining the lowest container usage possible. The approach was developed to increase small and medium-sized farmers’ ability to compete. Small and medium-sized farmers bring their products to an agricultural product collecting center, also known as a container loading facility. After that, skilled logistics companies distribute the goods. In order to convey the goods to the final clients in neighboring nations, the proper locations of the containing loading centers, the correct transportation option and the borders must be decided. The issue was identified as multi-echelon location–allocation sizing (MELLS), an NP-hard problem that cannot be handled in an efficient manner. To solve a real-world problem, however, efficient techniques must be supplied. AMIS, an artificial multiple intelligence system, was created to address the suggested issue. AMIS was developed with the goal of leveraging a variety of methods for local search and development. There are several well-known heuristics techniques employed in the literature, including the genetic algorithm (GA) and the differential evolution algorithm (DE). With respect to the improved solutions obtained, the computational results show that AMIS exceeds the present heuristics, outperforming DE and GA by 9.34% and 10.95%, respectively. Additionally, the system’s farmers made a total of 15,236,832 THB in profit, with an average profit per container of 317,434 THB and an average profit per farmer of 92,344.44 THB per crop. The container loading center uses 48 containers, with a 5.33 container average per container loading center (CLC). The farmers’ annual revenues were previously less than 88,402 THB per family per year, so we can predict that the new network may increase customers’ annual income by 4.459% for each crop.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Decision Variablesmentioning

confidence: 99%

A Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)

et al. 2022

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“…Current worldwide products employing sensors with varying spatial resolutions, such as MODIS (250 m-500 m), especially the burned area product (MCD641, FIRECCI, MCD451, etc. ), are popular for estimating air pollutant emissions from open biomass burning, especially in forest fire zones [5,[26][27][28]. These tools may not detect tiny fires or burned areas smaller than 1 km 2 , even though they generate air pollution that can affect land use [29].…”

Section: Introductionmentioning

confidence: 99%

The Spatial–Temporal Emission of Air Pollutants from Biomass Burning during Haze Episodes in Northern Thailand

Paluang,

Thavorntam,

Phairuang

2024

Fire

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Air pollutants from biomass burning, including forest fires and agricultural trash burning, have contributed significantly to the pollution of the Asian atmosphere. Burned area estimates are variable, making it difficult to measure these emissions. Improving emission quantification of these critical air pollution sources requires refining methods and collecting thorough data. This study estimates air pollutants from biomass burning, including PMs, NOX, SO2, BC, and OC. Machine learning (ML) with the Random Forest (RF) method was used to assess burned areas in Google Earth Engine. Forest emissions were highest in the upper north and peaked in March and April 2019. Air pollutants from agricultural waste residue were found in the lower north, but harvesting seasons made timing less reliable. Biomass burning was compared to the MODIS aerosol optical depth (AOD) and Sentinel-5P air pollutants, with all comparisons made by the Pollution Control Department (PCD) Thailand air monitoring stations. Agro-industries, mainly sugar factories, produce air pollutants by burning bagasse as biomass fuel. Meanwhile, the emission inventory of agricultural operations in northern Thailand, including that of agro-industry and forest fires, was found to have a good relationship with the monthly average levels of ambient air pollutants. Overall, the information uncovered in this study is vital for air quality control and mitigation in northern Thailand and elsewhere.

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“…As a result, dust and smoke accumulated from combustion and pollution accumulate in urban areas. Most of the sources of PM2.5 come from many factors such as vehicles, forest fires, open burning of garbage and agricultural wastes [3,4,5]. Therefore, monitoring PM2.5 surface concentration is important to control and mitigate air pollution problems.…”

Section: Introductionmentioning

confidence: 99%

Estimation of PM2.5 surface concentrations in the North-eastern and Eastern regions of Thailand from NASA’s MERRA2 satellite data

Chidburee

Nusit²,

Tantanee³

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The study aims to monitor PM2.5 concentrations in the north-eastern and eastern regions of Thailand for the past 5 years (from 2017 to 2021) from satellite observations for monitoring air quality. To ensure the PM2.5 concentrations from satellite data of NASA’s MEERA2 products, it is necessary to evaluate and analyze the relationship between PM2.5 data from satellite observations and higher precision data from ground-based stations of the Pollution Control Department (PCD) in terms of various statistical values, such as squared correlation coefficient (R2), mean (Bias) and Root Mean Square Error (RMSE). Then, the estimation of the monthly average for PM2.5 concentrations was performed using satellite data of MERRA2 products by bias correction method with twenty PCD’s ground stations. The result shows that before correcting the relationship value is relatively low, the performance of estimating PM2.5 concentrations from satellite data needs to be improved from the monthly average using the bias correction method. After bias-corrected, a correlation coefficient (R2) value of 0.73 was improved from 0.56. A mean (Bias) of 0.03 µg/m3 and an RMSE of 6.39 µg/m3 were decreased. It is clear that bias correction can help to improve estimating PM2.5 concentrations from MEERA2 products.

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Spatial and Temporal Distribution of Biomass Open Burning Emissions in the Greater Mekong Subregion

Cited by 9 publications

References 51 publications

A Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)

A Novel Artificial Multiple Intelligence System (AMIS) for Agricultural Product Transborder Logistics Network Design in the Greater Mekong Subregion (GMS)

The Spatial–Temporal Emission of Air Pollutants from Biomass Burning during Haze Episodes in Northern Thailand

Estimation of PM2.5 surface concentrations in the North-eastern and Eastern regions of Thailand from NASA’s MERRA2 satellite data

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