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

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.

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

2020 ◽  
Vol 12 (23) ◽  
pp. 10049
Author(s):  
Teerachai Amnuaylojaroen ◽  
Jirarat Inkom ◽  
Radshadaporn Janta ◽  
Vanisa Surapipith
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Biomass Burning ◽  
Model Performance ◽  
Weather Research And Forecasting ◽  
Potential Contribution ◽  
Northern Thailand ◽  
Range Transport ◽  
Control Department ◽  
Asian Monsoons

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.

Study of Air Pollution and Atmospheric Stability in Ciuc Basin - Romania

2017 ◽  
Vol 68 (8) ◽  
pp. 1763-1767 ◽  
Author(s):  
Robert Szep ◽  
Reka Keresztes ◽  
Attila Korodi ◽  
Szende Tonk ◽  
Mihaela Emanuela Craciun
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Negative Correlation ◽  
Air Pollutants ◽  
Richardson Number ◽  
Atmospheric Stability ◽  
Traffic Intensity ◽  
Concentration Increase ◽  
Obukhov Length ◽  
High Concentrations

The atmospheric stability plays an important role in the accumulation of air pollutants and greatly influences their degradation, dispersion and deposition. These atmospheric qualities can be determined with various methods (Richardson number, Monin - Obukhov length, SRDT method) and the pollutant concentration increase demonstrates the atmospheric stability. In this study the cold periods were the most stable as well the PM10 and CO pollutants had high concentrations. Between these two pollutants the correlation is high because their sources are the same: transport and biomass burning. The study of hourly averages highlighted the importance of traffic intensity since the concentration variation follows the traffic intensity. An increase in the wind speed in the basin results in the pollutants concentrations decrease, the negative correlation with the temperature indicating the importance of the photochemical processes.

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

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.

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

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.

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)

2020 ◽  
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

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

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

scholarly journals Measurement report: Effect of wind shear on PM<sub>10</sub> concentration vertical structure in urban boundary layer in a complex terrain

2021 ◽  
Author(s):  
Piotr Sekuła ◽  
Anita Bokwa ◽  
Jakub Bartyzel ◽  
Bogdan Bochenek ◽  
Łukasz Chmura ◽  
...  
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Case Studies ◽  
Vertical Profile ◽  
Wind Shear ◽  
Transitional Zone ◽  
Model Data ◽  
Profile Modification ◽  
Low Wind Speed ◽  
Clean Air

Abstract. The paper shows wind shear impact on PM10 vertical profiles, in Kraków, southern Poland. The data used consist of background data for two cold seasons (Sep. 2018 to Apr. 2019, and Sep. 2019 to Apr. 2020), and data for several case studies from November 2019 to March 2020. The data is composed of PM10 measurements, model data, and wind speed and direction data. The background model data come from operational forecast results of AROME model. PM10 concentration in the vertical profile was measured with a sightseeing balloon. Significant spatial variability of wind field was found. The case studies represent the conditions with much lower wind speed and a much higher PM10 levels than the seasonal average. The inversions were much more frequent than on average, too. Wind shear turned out to be the most important factor in terms of PM10 vertical profile modification. It is generated due to the relief impact, i.e. the presence of a large valley, blocked on one side with the hills. The analysis of PM10 profiles from all flights allows to distinguish three vertical zones of potential air pollution hazard within the valley (about 100 m deep) and the city of Kraków: 1. up to about 60 m a.g.l. – the zone where during periods of low wind speed, air pollution is potentially the highest and the duration of such high levels is the longest, i.e. the zone with the worst aerosanitary conditions; 2. about 60–100 m a.g.l. – transitional zone where the large decrease of PM10 levels with height is observed; 3. above 100–120 m a.g.l. – the zone where air quality is significantly better than in the zone 1, either due to the increase of the wind speed, or due to the wind direction change and advection of different, clean air masses.

scholarly journals Assessment of PM10 in Aurangabad City of Central India

2016 ◽  
Vol 5 (2) ◽  
pp. 61-74 ◽  
Author(s):  
Geetanjali Kaushik ◽  
Arvind Chel ◽  
Sangeeta Shinde ◽  
Ashish Gadekar
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Tall Buildings ◽  
Central India ◽  
Air Pollutant ◽  
Traffic Density ◽  
High Pollution ◽  
High Concentrations ◽  
The City

Almost 670 million people comprising 54.5% of our population reside in regions that do not meet the Indian NAAQS for fine particulate matter. Numerous studies have revealed a consistent correlation for particulate matter concentration with health than any other air pollutant. Aurangabad city a rapidly growing city with population of 1.5 million is home to five major industrial areas, the city is also known for its historical monuments which might also be adversely affected from air pollution. Therefore, this research aims at estimating PM10 concentrations at several locations across Aurangabad. The concentration of PM10 was highest at the Railway Station followed by Waluj (an industrial zone) and City chowk is the centre of the city which has high population, tall buildings, few open spaces which causes high congestion and does not allow the particulates to disperse. Other locations with high concentrations of PM are Mill corner, Harsul T-point, Kranti Chowk, Seven Hill, TV centre and Beed Bye pass. All these locations have narrow roads, high traffic density, poor road condition with pot holes and few crossing points which cause congestion and vehicle idling which are responsible for high pollution. Therefore, it is evident that air pollution is a serious issue in the city which may be further aggravated if it is not brought under control. Hence, strategies have to be adopted for combating the menace of air pollution.INTERNATIONAL JOURNAL OF ENVIRONMENTVolume-5, Issue-2, March-May 2016, Page :61-74

scholarly journals Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

2018 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Thomas M. Hamill ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Model Performance ◽  
The United States ◽  
Numerical Weather Prediction Model ◽  
Post Processing ◽  
Skill Scores ◽  
Ramp Events

Abstract. Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to twelve hours of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80-m wind speed observations from towers in Boulder, Colorado and near the Columbia River Gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method at predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake Shuffle method yields the highest skill at predicting ramp events for these data sets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO site using any of the multivariate methods, because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.

Analysis of Idealized Tropical Cyclone Simulations Using the Weather Research and Forecasting Model: Sensitivity to Turbulence Parameterization and Grid Spacing

2009 ◽  
Vol 137 (2) ◽  
pp. 745-765 ◽  
Author(s):  
Kevin A. Hill ◽  
Gary M. Lackmann
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Maximum Intensity ◽  
Weather Research And Forecasting ◽  
Grid Spacing ◽  
Exchange Coefficient ◽  
Model Sensitivity ◽  
Wind Speeds ◽  
Horizontal Grid ◽  
Weather Research

Abstract The Weather Research and Forecasting Advanced Research Model (WRF-ARW) was used to perform idealized tropical cyclone (TC) simulations, with domains of 36-, 12-, and 4-km horizontal grid spacing. Tests were conducted to determine the sensitivity of TC intensity to the available surface layer (SL) and planetary boundary layer (PBL) parameterizations, including the Yonsei University (YSU) and Mellor–Yamada–Janjic (MYJ) schemes, and to horizontal grid spacing. Simulations were run until a quasi-steady TC intensity was attained. Differences in minimum central pressure (Pmin) of up to 35 hPa and maximum 10-m wind (V10max) differences of up to 30 m s−1 were present between a convection-resolving nested domain with 4-km grid spacing and a parent domain with cumulus parameterization and 36-km grid spacing. Simulations using 4-km grid spacing are the most intense, with the maximum intensity falling close to empirical estimates of maximum TC intensity. Sensitivity to SL and PBL parameterization also exists, most notably in simulations with 4-km grid spacing, where the maximum intensity varied by up to ∼10 m s−1 (V10max) or ∼13 hPa (Pmin). Values of surface latent heat flux (LHFLX) are larger in MYJ than in YSU at the same wind speeds, and the differences increase with wind speed, approaching 1000 W m−2 at wind speeds in excess of 55 m s−1. This difference was traced to a larger exchange coefficient for moisture, CQ, in the MYJ scheme. The exchange coefficients for sensible heat (Cθ) and momentum (CD) varied by &lt;7% between the SL schemes at the same wind speeds. The ratio Cθ/CD varied by &lt;5% between the schemes, whereas CQ/CD was up to 100% larger in MYJ, and the latter is theorized to contribute to the differences in simulated maximum intensity. Differences in PBL scheme mixing also likely played a role in the model sensitivity. Observations of the exchange coefficients, published elsewhere and limited to wind speeds &lt;30 m s−1, suggest that CQ is too large in the MYJ SL scheme, whereas YSU incorporates values more consistent with observations. The exchange coefficient for momentum increases linearly with wind speed in both schemes, whereas observations suggest that the value of CD becomes quasi-steady beyond some critical wind speed (∼30 m s−1).

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