Long Range Transport of Southeast Asian PM2.5 Pollution to Northern Thailand during High Biomass Burning Episodes

This paper aims to investigate the potential contribution of biomass burning in PM2.5 pollution in Northern Thailand. We applied the coupled atmospheric and air pollution model which is based on the Weather Research and Forecasting Model (WRF) and a Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). The model output was compared to the ground-based measurements from the Pollution Control Department (PCD) to examine the model performance. As a result of the model evaluation, the meteorological variables agreed well with observations using the Index of Agreement (IOA) with ranges of 0.57 to 0.79 for temperature and 0.32 to 0.54 for wind speed, while the fractional biases of temperature and wind speed were 1.3 to 2.5 °C and 1.2 to 2.1 m/s. Analysis of the model and hotspots from the Moderate Imaging Spectroradiometer (MODIS) found that biomass burning from neighboring countries has greater potential to contribute to air pollution in northern Thailand than national emissions, which is indicated by the number of hotspot locations in Burma being greater than those in Thailand by two times under the influence of two major channels of Asian Monsoons, including easterly and northwesterly winds that bring pollutants from neighboring counties towards northern Thailand.

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Long Range Transport of Southeast Asia PM2.5 Pollution to Northern Thailand during High Biomass Burning Episode

10.20944/preprints202010.0157.v1 ◽

2020 ◽

Author(s):

Teerachai Amnuaylojaroen ◽

Jirarat Inkom

Keyword(s):

Air Pollution ◽

Wind Speed ◽

Southeast Asia ◽

Model Performance ◽

Weather Research And Forecasting ◽

Northern Thailand ◽

Range Transport ◽

Control Department ◽

High Concentrations ◽

Transport Emission

This paper aims to investigate the airflow that can transport emission sources of PM2.5 from neighboring countries to contribute to air pollution in northern Thailand. We applied the coupled atmospheric and air pollution model which is based on the Weather Research and Forecasting Model (WRF) and a Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). The model output was compared to the ground-based measurement from the Pollution Control Department (PCD) to examine model performance. As a result of model evaluation, the meteorological variables fairly agreed well compared to observation with Index of Agreement (IOA) in ranges of 0.57 to 0.79 for temperature and 0.32 to 0.54 for wind speed, while the fractional bias of temperature and wind speed were 1.3 to 2.5 °C and 1.2 to 2.1 m/s. Burma was a country that contributed much of hotpot locations by 37% of the entire hotspot locations of Southeast Asia in March. The influence of the Asian Monsoon can bring pollutants from neighboring countries such as Burma and Laos toward northern Thailand in March that likely contribute to high concentrations of PM2.5 in northern Thailand.

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Impact of Biomass Burning on Ozone, Carbon Monoxide, and Nitrogen Dioxide in Northern Thailand

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.641877 ◽

2021 ◽

Vol 9 ◽

Author(s):

Suratsawadee Khodmanee ◽

Teerachai Amnuaylojaroen

Keyword(s):

Biomass Burning ◽

Model Simulation ◽

Model Performance ◽

Weather Research And Forecasting ◽

Grid Spacing ◽

Northern Thailand ◽

Temperature And Precipitation ◽

Haze Pollution ◽

Simulation Domain ◽

The Impact

The problem of smoke haze pollution in Northern Thailand affects both the environment and residents. The main sources of smoke are wildfires and open burning during the dry season, which release many pollutants, especially surface O3, impacting health and causing an air pollution crisis. The aim of this research was to study the impact of biomass burning on the surface O3, CO, and NO2 levels in Northern Thailand using the Weather Research and Forecasting Model with Chemistry (WRF-Chem). The simulation domain was configured with two domains with a grid spacing of 50 and 10 km in March 2014. To elucidate the effect of biomass burning, the model simulation was conducted for two cases: 1) a simulation with anthropogenic, biogenic, and biomass burning emissions; and 2) a simulation excluding biomass burning emissions. Owing to the model performance, the diurnal temperature and precipitation were consistent with observations, as indicated by the index of agreement (IOA) ranges of 0.74–0.76, while those of O3, CO, and NO2 were in the ranges of 0.12–0.71. The results show that biomass burning increased O3, CO, and NO2 levels by 9, 51, and 96%, respectively.

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Seasonal Patterns and Trends of Air Pollution in The Upper Northern Thailand from 2004 to 2018

10.21203/rs.3.rs-914607/v1 ◽

2021 ◽

Author(s):

Sarawut Sukkhum ◽

Apiradee Lim ◽

Rattikan Saelim ◽

Thammasin Ingviya

Keyword(s):

Air Pollution ◽

Pollution Control ◽

Air Pollutants ◽

Air Pollutant ◽

Seasonal Patterns ◽

Average Increase ◽

Concentration Data ◽

Northern Thailand ◽

The Past ◽

Control Department

Abstract The objective of this study was to investigate the seasonal patterns and trends of air pollutants in upper northern Thailand (UNT) from 2004 to 2018. The hourly air pollutant concentration data recorded from 6 monitoring stations in the UNT were obtained from the Pollution Control Department, Ministry of Natural Resources and Environment of Thailand. Cubic splines were used to assess seasonal patterns and trends of air pollutants. Linear regression was used to estimate the average increase in concentrations of air pollutants at each monitoring station. The results exhibited seasonal patterns for CO, NOX, NO2, O3, and PM10, in all stations while SO2 only in one station in Lampang and all stations in Chiangmai. The concentrations of these pollutants rose during August and September and reached peak levels between March and April. In the past 15 years, the levels of overall CO, O3, and SO2 in the UNT had significantly increased, on average of 0.032 ppm, 0.012 ppb, and 0.017 ppb, respectively. In contrast, NO2, NOX, and PM10 had significantly decreased on average of 0.012 ppb, 0.011 ppb, and 0.016 mg/m3, respectively. In conclusion, it should be of concern for such activities that related to air pollutants variation accordingly.

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AIR POLLUTION EPISODES ASSOCIATED WITH LONG RANGE TRANSPORT OF BIOMASS BURNING PRODUCTS

10.3997/2214-4609-pdb.160.sbgf457 ◽

2005 ◽

Author(s):

Cláudia Boian ◽

Volker W. J. H. Kirchhoff

Keyword(s):

Air Pollution ◽

Long Range ◽

Biomass Burning ◽

Long Range Transport ◽

Range Transport ◽

Air Pollution Episodes ◽

Pollution Episodes

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AIR POLLUTION EPISODES ASSOCIATED WITH LONG RANGE TRANSPORT OF BIOMASS BURNING PRODUCTS

10.1190/sbgf2005-457 ◽

2005 ◽

Author(s):

Cláudia Boian ◽

W. J. H. Volker Kirchhoff

Keyword(s):

Air Pollution ◽

Long Range ◽

Biomass Burning ◽

Long Range Transport ◽

Range Transport ◽

Air Pollution Episodes ◽

Pollution Episodes

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Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics (Preprint)

10.2196/preprints.19331 ◽

2020 ◽

Cited By ~ 1

Author(s):

Mario Coccia

Keyword(s):

Air Pollution ◽

Wind Speed ◽

Critical Role ◽

Meteorological Condition ◽

Meteorological Conditions ◽

Lessons Learned ◽

Transmission Dynamics ◽

Critical Threshold ◽

Viral Infectivity ◽

Low Wind Speed

BACKGROUND Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death. OBJECTIVE This study has two goals. The first is to explain the main factors determining the diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats with of accelerated viral infectivity in society. METHODS Correlation and regression analyses on on data of N=55 Italian province capitals, and data of infected individuals at as of April 2020. RESULTS The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 in specific environments is due to two mechanisms given by: air pollution-to-human transmission and human-to-human transmission; in particular, the mechanisms of air pollution-to-human transmission play a critical role rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society. CONCLUSIONS Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19. CLINICALTRIAL not applicable

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Air pollution forecasting application based on deep learning model and optimization algorithm

Clean Technologies and Environmental Policy ◽

10.1007/s10098-021-02080-5 ◽

2021 ◽

Author(s):

Azim Heydari ◽

Meysam Majidi Nezhad ◽

Davide Astiaso Garcia ◽

Farshid Keynia ◽

Livio De Santoli

Keyword(s):

Air Pollution ◽

Wind Speed ◽

Power Plant ◽

Air Temperature ◽

Short Term Memory ◽

Combined Cycle ◽

Short Term ◽

Term Memory ◽

Proposed Model ◽

Long Short Term Memory

AbstractAir pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO2 and SO2 by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO2, and SO2 for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO2 and SO2); type (2) includes just lagged values of the output variables (NO2 and SO2). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract

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Characteristics and formation mechanism of a heavy air pollution episode caused by biomass burning in Chengdu, Southwest China

The Science of The Total Environment ◽

10.1016/j.scitotenv.2013.12.069 ◽

2014 ◽

Vol 473-474 ◽

pp. 507-517 ◽

Cited By ~ 60

Author(s):

Yuan Chen ◽

Shao-dong Xie

Keyword(s):

Air Pollution ◽

Formation Mechanism ◽

Biomass Burning ◽

Southwest China ◽

Pollution Episode

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Forecasting carbon monoxide on a global scale for the ATom-1 aircraft mission: insights from airborne and satellite observations and modeling

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-10955-2018 ◽

2018 ◽

Vol 18 (15) ◽

pp. 10955-10971 ◽

Cited By ~ 1

Author(s):

Sarah A. Strode ◽

Junhua Liu ◽

Leslie Lait ◽

Róisín Commane ◽

Bruce Daube ◽

...

Keyword(s):

Biomass Burning ◽

Optical Thickness ◽

Chemical Reactivity ◽

Model Performance ◽

Global Scale ◽

Satellite Observations ◽

Aerosol Optical Thickness ◽

Boreal Summer ◽

Large Contribution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. The first phase of the Atmospheric Tomography Mission (ATom-1) took place in July–August 2016 and included flights above the remote Pacific and Atlantic oceans. Sampling of atmospheric constituents during these flights is designed to provide new insights into the chemical reactivity and processes of the remote atmosphere and how these processes are affected by anthropogenic emissions. Model simulations provide a valuable tool for interpreting these measurements and understanding the origin of the observed trace gases and aerosols, so it is important to quantify model performance. Goddard Earth Observing System Model version 5 (GEOS-5) forecasts and analyses show considerable skill in predicting and simulating the CO distribution and the timing of CO enhancements observed during the ATom-1 aircraft mission. We use GEOS-5's tagged tracers for CO to assess the contribution of different emission sources to the regions sampled by ATom-1 to elucidate the dominant anthropogenic influences on different parts of the remote atmosphere. We find a dominant contribution from non-biomass-burning sources along the ATom transects except over the tropical Atlantic, where African biomass burning makes a large contribution to the CO concentration. One of the goals of ATom is to provide a chemical climatology over the oceans, so it is important to consider whether August 2016 was representative of typical boreal summer conditions. Using satellite observations of 700 hPa and column CO from the Measurement of Pollution in the Troposphere (MOPITT) instrument, 215 hPa CO from the Microwave Limb Sounder (MLS), and aerosol optical thickness from the Moderate Resolution Imaging Spectroradiometer (MODIS), we find that CO concentrations and aerosol optical thickness in August 2016 were within the observed range of the satellite observations but below the decadal median for many of the regions sampled. This suggests that the ATom-1 measurements may represent relatively clean but not exceptional conditions for lower-tropospheric CO.

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