Analysis Of Wind Direction Research Articles

Characteristics and sources of atmospheric ammonia at the SORPES station in the western Yangtze river delta of China

Ammonia (NH3) is an important air pollutant with crucial impacts on air quality, ecosystems and climate change. However, NH3 is not included in routine air quality monitoring and the availability of long-term NH3 measurements is still limited, resulting in large uncertainties in our knowledge of the spatial distribution and sources of NH3. Here we performed 1 year (March 2021–February 2022) of atmospheric NH3 measurements at a regional background station, the Station for Observing Regional Processes of the Earth System (SORPES) in the western Yangtze River Delta of China. We found that the annual mean NH3 concentration was 12.2 ± 4.6 ppb and exhibited apparent seasonal variations, with a maximum in June and a minimum in February, influenced by agricultural activities, air temperature, gas-particle partitioning and precipitation. Moreover, air temperature and absolute humidity correlated well with NH3, indicating that they are important factors in influencing NH3 levels. The diurnal variation of NH3 showed a single peak in the morning and higher concentrations during the day. In spring and autumn, the NH3 morning peak can be attributed to dew evaporation. The air mass backward trajectory, local wind direction and velocity analysis suggested that NH3 was influenced by both local emissions and regional transport from nearby cities. During the COVID-19 lockdown, a strong reduction of NOx (−77%) and a weak reduction of NH3 (−14%) were observed compared to the pre-lockdown, highlighting that traffic emissions have a minor impact on NH3 at the SORPES station. Our results provide more insights into the characteristics and sources of atmospheric NH3 in background regions influenced by mixed air pollution plumes.

Wind Turbine Fire Prevention System Using Fuzzy Rules and WEKA Data Mining Cluster Analysis

With the rapid expansion of the supply of renewable energy in accordance with the global energy transition policy, the wind power generation industry is attracting attention. Subsequently, various wind turbine control technologies have been widely developed and applied. However, there is a lack of research on optimal pitch control, which detects wind direction and changes the rotation angle of the blade in real time. In areas where the wind speed is not strong, such as South Korea, it is necessary to maintain the optimal angle in real time so that the rotating surface of the blade can face the wind direction. In this study, optimal pitch control was performed through real-time analysis of wind speed, direction, and temperature, which is the core of wind turbine maintenance, using fuzzy rules using FIS (Fuzzy Interface System) and WEKA data mining cluster analysis techniques. In order to prevent fires caused by the over-current of wind turbines, over-current control methods such as VCB (Vacuum Circuit Breaker) utilization, prototype utilization such as a modular MCB (Main Circuit Breaker) incorporating VI (Vacuum Interrupter), and vacuum degree change analysis methods using a PD (Partial Discharge) signal were proposed. The optimal control technique for wind turbine parts and facilities was put forth after judging and predicting the annual average wind distribution suitable for wind power generation using HRWPRM (Korea’s High-Resolution Wind Power Resource Maps). Finally, the various wind turbine control methods carried out in this study were confirmed through computer simulation, such as remote diagnosis and early warning issuance, prediction of power generation increase and decrease situation, and automatic analysis of wind turbine efficiency.

Optimizing Wind Farm Layouts with Genetic Algorithms (Enhancing Efficiency in Wind Energy Planning and Utilization in Bosnia and Herzegovina)

This paper proposes a genetic algorithm-based approach to optimize wind farm layouts in Bosnia and Herzegovina, a country with a high potential for wind energy production. The primary objective is to maximize the total power output derived from the wind farm while concurrently minimizing the wake effects resulting from turbine interactions. This balance is pivotal in ensuring the optimal utilization of wind energy resources. In this study, three different wind data scenarios are considered: a single wind direction with overall average velocity, the most prevalent wind direction with overall average velocity, and an all-encompassing wind direction analysis incorporating a weighted objective function. The results of the study suggest that the genetic algorithm is highly effective in identifying optimal solutions across each scenario. This serves as a testament to the algorithm's accuracy, robustness, and applicability in tackling real-world problems, thereby marking a significant step forward in the realm of wind farm optimization. The limitations of this research include the use of a simplified wake model and a fixed turbine type. The implications of this research include the potential for increasing the efficiency and profitability of wind farms in Bosnia and Herzegovina, as well as informing future research on more complex and realistic optimization problems.

Copula‐based joint distribution analysis of wind speed and wind direction: Wind energy development for Hong Kong

AbstractAccurate and reliable assessment of wind energy potential has important implication to the wind energy industry. Most previous studies on wind energy assessment focused solely on wind speed, whereas the dependence of wind energy on wind direction was much less considered and documented. In this paper, a copula‐based method is proposed to better characterize the direction‐related wind energy potential at six typical sites in Hong Kong. The joint probability density function (JPDF) of wind speed and wind direction is constructed by a series of copula models. It shows that Frank copula has the best performance to fit the JPDF at hilltop and offshore sites while Gumbel copula outperforms other models at urban sites. The derived JPDFs are applied to estimate the direction‐related wind power density at the considered sites. The obtained maximum direction‐related wind energy density varies from 41.3 W/m2 at an urban site to 507.9 W/m2 at a hilltop site. These outcomes are expected to facilitate accurate micro‐site selection of wind turbines, thereby improving the economic benefits of wind farms in Hong Kong. Meanwhile, the developed copula‐based method provides useful references for further investigations regarding direction‐related wind energy assessments at various terrain regions. Notably, the proposed copula‐based method can also be applied to characterize the direction‐related wind energy potential somewhere other than Hong Kong.

Modelling the Wind Speed Using Exponentiated Weibull Distribution: Case Study of Poprad-Tatry, Slovakia

In the paper, we statistically analysed data on the average hourly wind speed obtained from the meteorological station Poprad (located at the Poprad-Tatry airport, the Prešov region, Northern Slovakia) for the period 2005–2021. High altitude and rough mountainous terrain influence the weather conditions considerably and are a source of occasional weather risks. Finding an appropriate wind speed distribution for modelling the wind speed data is therefore important to determine the wind profile at this particular location. In addition to the commonly used two- and three-parameter Weibull distribution, a more flexible exponentiated Weibull (EW) distribution was applied to model the wind speed. Based on the results of the goodness-of-fit criteria (the Kolmogorov–Smirnov test, the Anderson–Darling test, Akaike’s and Bayesian information criteria, the root mean square error, and the coefficient of determination), the EW distribution obtained a significantly better fit to seasonal and monthly wind speed data, especially around the peaks of the data. The EW distribution also proved to be a good model for data with high positive skewness. Therefore, we can recommend the EW distribution as a flexible distribution for modelling a dataset with extremely strong winds or outliers in the direction of the right tail. Alongside the wind speed analysis, we also provided the wind direction analysis, finding out that the most prevailing direction was west (W)—with an occurrence rate of 34.99%, and a mean wind speed of 3.91 m/s, whereas the northern (N) direction featured the lowest occurrence rate of only 4.45% and the mean wind speed of 1.99 m/s.

Meteorological Approach in the Identification of Local and Remote Potential Sources of Radon: An Example in Northern Iberian Peninsula.

This paper presents a meteorological approach to identify local and remote sources driving the variability of surface daily radon concentrations. To this purpose, hourly 222Rn concentration and surface meteorological measurements, and air mass trajectories at Bilbao station (northern Iberian Peninsula) during the period 2017-2018 have been taken as reference. To investigate the potential transport pathways and potential 222Rn sources, the backward trajectory cluster analysis, trajectory sector analysis (TSA), and potential source contribution function (PSCF) are applied. On average, the diurnal 222Rn cycle shows the expected behaviour, with larger concentrations during the night and minimum concentrations during the daylight hours, with differences in the seasonal amplitudes. According to daily differences between maximum and baseline values, 222Rn daily cycles were grouped into six groups to identify meteorological conditions associated with each amplitude, and potential source areas and transport routes of 222Rn over Bilbao. The trajectory cluster and the TSA method show that the main airflow pathways are from the south, with small displacement, and the northeast, while the analysis of surface wind speed and direction indicates that the highest amplitudes of 222Rn concentrations are registered under the development of sea-land breezes. The PSCF method identified south-western and north-eastern areas highly contributing to the 222Rn concentration. These areas are confirmed by comparing with the radon flux map and the European map of uranium concentration in soil. The results have demonstrated the need in combining the analysis of local and regional/synoptic factors in explaining the origin and variability of 222Rn concentrations.

Enhanced resource assessment and atmospheric monitoring of the research wind farm WiValdi

Prior to the installation of wind turbines at the Krummendeich research wind farm (referred to as WiValdi) which is developed by the German Aerospace Center (DLR), we conducted measurements with a Doppler wind lidar (DWL) and a microwave radiometer (MWR) for 16 months, starting in November 2020. The remote sensing data was validated against radiosonde measurements in a short-term campaign in September 2021. We present a statistical analysis of wind speed, direction, shear and veer as derived from the DWL as well as static stability from temperature retrievals of the MWR in comparison to model results of the New European Wind Atlas (NEWA). The observational data from 2021 shows a higher occurence rate of winds from northwest in comparison to the longer-term statistics of NEWA (2011-2018). From the overall statistics, cases with the occurence of low-level jets (LLJ) are detected and separated. The LLJs appear in 49% of the observed nights and predominantly in conditions with seaward wind direction. The LLJ increases shear and veer in the rotor swept area and thus the potential loads significantly. Future research at WiValdi will allow to analyse the effects of such load cases on the wind turbines in detail.

Wind and Gust Forecasts Assessment of Weather Research and Forecast (WRF) Model in Córdoba, Argentina

WRF wind forecasts from four operative schemes used by OHMC (Observatorio HidroMeteorológico de Córdoba), a test scheme (WRF-E) and two daily runs with 4 km horizontal resolution were analyzed. Wind simulations were compared with measurements from eight ground stations with anemometers at 10 m high during the period from June, 2019 to June, 2020. WRF-E incorporates more vertical levels, and an activated topo_wind option. The wind speed results show that WRF overestimates wind speed at most stations and the WRF-E model reduces the BIAS and the RMSE when compared with the operational models. The wind direction analysis shows that the higher the wind speed is, the more accurate the models are. In addition, a wind gust forecasting has been implemented and evaluated in this work. Wind gust correlation coefficient values are between 0.3 and 0.6, RMSE is between 3 and 5 m/s, and a positive BIAS(<2 m/s) at most stations.

Analysis of Wind Direction and Speed of Rainfall Distribution in Tangerang Regency

The wind is the horizontal movement of air masses. Wind can occur if there is any difference in pressure between one place to another place. The pattern from the wind movement is the direction and velocity. That pattern becomes a part to influence the rainfall. This research is to determine the direction and velocity of wind who more dominant happens in the Tangerang district using a windrose method and to determine the relationship between the direction and velocity of wind to the distribution of rainfall with multiple linear regression analysis. This research uses secondary data which is daily data during the period of January 2011 to December 2020 and then the data will be processed using WRPLOT and Minitab software. Based on the results using the windrose method the data obtained that the direction of the wind is more dominant blows from the west to the east with an average wind speed of 2.93 m/s. Through the Kolmogorov-Smirnov test, the rainfall distribution test in the Tangerang district is normally distributed. The results of multiple linear regression show that wind has a negative effect on rainfall. The highest rainfall occurred in February 2020 as much as 556 mm with the wind blowing from the west-northwest.

Industrial Source Contributions and Health Risk Assessment of Fine Particle-Bound Polycyclic Aromatic Hydrocarbons (PAHs) during Spring and Late Summer in the Baoshan Area, Shanghai

The main objective of this study was to examine the chemical characteristics, possible sources, and health risks of fine particle-bound Polycyclic Aromatic Hydrocarbons (PAHs) in the Baoshan area of Shanghai. Here, ambient particles with five-size ranges were collected during the spring and late summer of 2017. The PAHs were determined by the Gas Chromatography-Mass Spectrometry (GC-MS). Our results showed that the average mass concentration of 13 species of PAHs in spring and in late summer was 4.83 (1.88~12.1) ng/m3 and 4.27 (2.09~5.75) ng/m3 in Total Suspended Particles (TSPs), respectively. The higher PAH ratios (PM1.1/TSPs) indicated that PAHs are mainly concentrated in PM1.1, especially in late summer. The values of BaA/(BaA+CHR) were under 0.50 and IcdP/(IcdP+BghiP) were in range from 0.20 to 0.50 for TSP and PM1.1, suggesting that petroleum combustion and diesel emissions could be considered as key sources of PAHs, which tend to be associated with PM1.1. Moreover, the Principal Component Analysis (PCA) in PM1.1 identified the main PH sources, which include stationary and diesel emissions. The air mass backward trajectories and wind direction analysis showed that air masses were mainly derived from marine sources across the local industry area in late summer. Individual Carcinogenic Risk Inhalation (ILCR) was over 10−6 among the total six age groups in both of the sampling periods in TSPs, indicating the possible carcinogenic risk, especially for children and the young age group. Toxic PAHs belong to Heavy Molecular Weight (HMW) PAHs, especially Benzo[a]pyrene (BaP). Compared with PM1.1–2.0, the Combustion-Derived PAHs group (COMPAHs) and Carcinogenic PAHs (CANPAHs) were highly concentrated in PM1.1. Stationary sources, such as the developed steel industry, made a great contribution to the level of PAHs, especially in late summer.

Source Apportionment and Health Risk Assessment of Heavy Metals in PM2.5 in Handan: A Typical Heavily Polluted City in North China

In order to determine the pollution sources and human health risks of metal elements in PM2.5, samples were collected by a large flow particulate matter sampler in the four seasons in 2013, 2015, and 2017 (January, April, July, and October). The mass concentrations of 10 metals (Ti, V, Cr, Mn, Ni, Cu, Zn, As, Cd, and Pb) were analyzed. The sources of heavy metals were identified by Unmix, and the potential non-carcinogenic/carcinogenic risk was evaluated. The influences of local and regional sources were also explored during the high-carcinogenic risk period (HCRP). The wind field and 72 h backward trajectories were performed to identify the potential local and regional sources in HCRP. The results showed that the average annual concentrations of PM2.5 in the urban area of Handan city were 105.14, 91.18, and 65.85 μg/m3 in 2013, 2015, and 2017, respectively. The average daily concentrations of the metals in PM2.5 in January were higher than that of April, July, and October. The average mass concentrations of the 10 heavy metal elements in PM2.5 were 698.26, 486.92, and 456.94 ng·m−3 in 2013, 2015, and 2017, respectively. The main sources of the metals in PM2.5 were soil dust sources, vehicular emissions, coal burning, and industrial activities. The carcinogenic risks of Cr and As were above 1 × 10−6 over the three years. Wind direction analysis showed that the potential local sources were heavy industry enterprises and the economic development zone. The backward trajectory analysis indicated that PM2.5 long transported from Shandong, Henan, and the surrounding cities of Handan had quite an impact on the heavy metals contained in the atmosphere of the studied area. The health risk assessment results demonstrated that the trend for non-carcinogenic risk declined, and there was no non-carcinogenic risk in 2017. However, the carcinogenic risk levels were high over the three years, particularly in January.

Influencing factors of PM2.5 and O3 from 2016 to 2020 based on DLNM and WRF-CMAQ

In this study, distributed lag nonlinear models (DLNM) were built to characterize the non-linear exposure-lag-response relationship between the concentration of PM2.5 and O3 and multiple influencing factors, including basic meteorological elements and precursors. Then, a stratified analysis of different years, seasons, pollution levels, and wind direction was conducted. DLNMs and coupled Weather Research and Forecasting Model-Community Multi-scale Air Quality Model (WRF-CMAQ) were used to evaluate PM2.5 and O3 changes attributed to meteorological conditions and anthropogenic emissions comparing 2020 with 2016. As DLNMs showed, PM2.5 pollution was promoted by low wind speed, high temperature, low humidity, and high concentrations of SO2, NO2, and O3, among which NO2 tended to be the dominant influencing factor. O3 pollution was promoted by low wind speed, high temperature, low humidity, high concentration of PM2.5 and low concentration of NO2, among which temperature tended to be the dominant influencing factor. Moreover, north-south and easterly winds showed the greatest contribution to PM2.5 and O3, respectively. Both DLNMs and CMAQ showed that anthropogenic factors alleviated PM2.5 pollution but aggravated O3 pollution in 2020 in comparison with 2016, so did meteorological factors, but with smaller impacts. And anthropogenic influences were more evident in heavily polluted seasons for both PM2.5 and O3. This research may help understand the influencing factors of PM2.5 and O3 and provide scientific guide for abatement policies. Moreover, the good consistency in the results obtained from DLNMs and CMAQ indicated the reliability of the two models.

PCA analysis of wind direction climate in the baltic states

Wind direction is one of the fundamental parameters of weather. In this study we investigate the wind direction climate 10 m above surface level in the Baltic States (Estonia, Latvia, Lithuania). The analysis of wind direction over larger regions is usually hindered by the fact that wind direction is a circular variable, which means that averaged values are meaningless. Here we show how Principal Component Analysis (PCA) can be applied to give a large scale overview of typical wind direction patterns in the region. Here we apply PCA to both observational and reanalysis data. The most significant wind direction patterns are detected in both synoptic scale and mesoscale, and we attempt to link the identified patterns with meteorological phenomena. In addition, the differences in the PCA results between observation and model data are analysed. The results show that PCA method is successful in identifying and ranking the wind direction climate features, leading to a complete and thorough investigation for the whole region that would be not possible by human researchers analysing individual distributions of wind direction.

Simulation and analysis of wind speed and direction of unmanned aerial vehicle route in the Beijing-Tianjin-Hebei region based on high resolution model

Simulation and analysis of wind speed and direction of unmanned aerial vehicle route in the Beijing-Tianjin-Hebei region based on high resolution model

Determination of Optimal Parametric Distribution and Technical Evaluation of Wind Resource Characteristics for Wind Power Potential at Jhimpir, Pakistan

The objective of this research work is to analyze wind characteristics and to assess wind power potential by selecting the best fit probability distribution function of Jhimpir Sindh Pakistan. This type of detailed investigation helps wind power generation companies in selecting suitable wind turbine and provides information of wind characteristics of potential site. Eight probability distribution functions are tested on the wind speed data from January 2015 to July 2018. Frequency bins of Weibull and Rayleigh distribution with maximum probabilities of 0.1210 and 0.1143 are most closest representation of our data. In order to, quantitatively analysis which distribution function is best fitting the local wind regime, we have applied the coefficient-of-determination, Kolmogorov-Smirnov, Chi square, Cramer-von Mises, Anderson-Darling tests along with Akaike information and Bayesian information criterion. These statistical test are used to rank the empirical distribution functions in order to identify two distribution function better fitting the actual wind speed data. After selecting two best fitted distribution functions, we analyze wind power potential and compare the error of wind power density based on these distribution functions (Weibull and Rayleigh). The power densities reported varied from 73.67 to 648.73W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Results indicate that power densities of Weibull and Rayleigh for the candidate site are 84.67-698.65W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 83.67-1021.4W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The highest error for Weibull and Rayleigh are 0.1850 and 0.5745, respectively. Whereas lowest error are 0.0178 and 0.0180, respectively. Complete analysis suggested that Weibull distribution function is the most suitable for Jhimpir Sindh Pakistan and the studied site is suitable for wind power production. In addition, comprehensive analysis of wind direction at the candidate site suggested that Eastern and Southeastern wind directions are predominant with 38.52% and 33.24% of the total time.

Impact of urban, suburban and industrial background on air pollution levels of dust substances in North-Eastern part of Krakow (Poland)

Air pollution in urban-industrial areas is caused by simultaneous impact of many factors, including different types of emission sources. Ambient air quality in Krakow is a crucial problem regarding the regularly occurring exceedances of limit values of particulate matter and some of its chemical compounds. This paper presents quantification of urban, suburban and industrial background of dust substances concentrations that are present in the industrialized area, located in the vicinity of scattered household and road traffic emission sources. There were included the concentrations of such substances as: particulate matter (PM10), benzo(a)pyrene, arsenic, cadmium, lead and nickel. The impact of daytime and season of the year (especially heating and non-heating season) on variability of air pollutant concentrations was examined. In order to distinguish between local and inflow background of air pollutants the additional meteorological data concerning wind speed and direction was considered. The performed analyses included application of statistical methods, among others principal component analysis (PCA). Some of the results were visualized via R programming environment, providing tools for air pollution data processing (openair package). The backward trajectories modelling using HYSPLIT model, allowed the validation of wind direction analyses. The conducted research revealed the strong dependence of air pollution background type influencing the measurement results on instantaneous wind direction.

Chemical characterization and source apportionment of fine and coarse atmospheric particulate matter in Doha, Qatar

Atmospheric particulate matter of two size fractions (i.e., PM2.5 and PM10) were sampled simultaneously at an urban site in Doha, Qatar, and characterized to measure chemical constituents (including trace elements, organic and elemental carbon (OC/EC), and water-soluble ions). The composition data were applied to mass closure analysis and Positive Matrix Factorization (PMF) model along with detailed wind direction analysis for quantifying and identifying the probable sources of both PM size fractions. The average concentrations of PM2.5 (40 μg m−3) and PM10 (146 μg m−3) were greatly exceeded the WHO air quality thresholds for 78–92% of the sampling days. Mass closure indicated the predominance of secondary inorganic aerosol (39%) and EC (7%) in fine PM, while mineral dust contributes 50% to the total mass of coarse particles. PMF identified six major sources of fine and coarse PM, including secondary inorganic aerosols, vehicular emissions, heavy oil combustion, marine aerosols, mineral and fugitive dust. The wind direction analysis indicated that both local and regional sources impact Qatar's air quality attributed to specific wind conditions. Two main sources of heavy oil combustion and secondary aerosols are closely related to regional emissions from shipping vessels and the petrochemical industry in the Gulf region. The desert dust transport mixed with local re-suspended dust and sea-salt particles also contributes considerably to the local PM concentrations. The significant contributions of regional anthropogenic and natural emissions pose a significant challenge in meeting local PM standards, signifying the importance of regional collaboration for better control policies on reducing transboundary air pollution.

Distributions, Source and Pollution Status of Heavy Metals of Urban Soil in Xining, China

In recent years, the topsoil has suffered from remarkable heavy metal contamination with a rapid development of urbanization and industrialization in Xining. However, there are less detailed studies on the heavy metals contamination in Xining topsoil. We collected 40 soils samples from the upper 5 cm of the soil layer and detected the concentrations of Pb, Cu, Zn, Cd, As, Sb, Cr, Ni in the soil by inductively coupled plasma-mass spectrometry (ICP-MS) and that of Hg was tested by mercury detector. Compared with Chinese other part of soil, the concentrations of heavy metals in urban soils of Xining were significantly higher, and the average values of Zn, Cd, Sb, Pb and Hg were 9.13, 72.96, 5.30, 6.13, and 8.23 times as high as their corresponding background values, respectively. On the basis of the results of the correlation coefficient analysis, principal component analysis (PCA), cluster analysis (CA), the spatial distribution pattern analysis and heavy metal ratio analysis, we discovered that Hg, As, Cu, Sb, Pb, Cd and Zn had similar patterns, and their hotspots were primarily associated with three industrial districts in Xining, especially the Ganhe Industrial District in the southwest of Xining, while Cr and Ni mainly came from the natural source (loess around Xining). Heavy metal enrichment factor (EF) further confirms the source identification and degree of contamination. Moreover, the wind direction analysis shows the southwest and northwest winds transport lots of pollutions from the industry districts to the urban zones. The results of this paper clearly highlight that all people in xining are in sore need of pulling together to control industrial emissions/site, and meanwhile remedy seriously contaminated urban soils.

Impact of Polycyclic Aromatic Hydrocarbons (PAHs) from an Asphalt Mix Plant in a Suburban Residential Area

Polycyclic aromatic hydrocarbons (PAHs), an important class of hazardous airborne pollutants, are mutagenic and carcinogenic substances known to be released during the paving of asphalt. In this study, PAHs emitted from an asphalt mix plant were analyzed to investigate the effects on a suburban residential area. Black carbon, organic carbon, elemental carbon, and PAHs in fine particulate matter (PM2.5) were analyzed in a village near the asphalt mix plant. The results of wind direction analysis revealed that the village was meteorologically affected by emissions from the asphalt mix plant. PAHs in PM2.5 ranged from 0.51 to 60.73 ng/m3, with an average of 11.54 ng/m3. Seasonal PAHs were highest in winter, followed in order by spring, autumn, and summer. The diagnostic ratios between PAHs indicate that the source of PAHs could be incomplete combustion of petrogenic origin. The maximum black carbon concentration in the intensive periods reaches up to 14.17 μg/m3 during mix plant operation periods. Seasonal ∑BaPTEF values based on Toxic Equivalence Factor were: winter (2.284 ng/m3), spring (0.575 ng/m3), autumn (0.550 ng/m3), and summer (0.176 ng/m3). The values are about 6.5 times higher than the concentration in another background area and more than three times higher than those in the capital city, Seoul, in the Republic of Korea. In conclusion, primary emissions from the point source can be considered the major contributor to pollution in the residential area.

Development of a noise-assisted multivariate empirical mode decomposition framework for characterizing PM 2.5 air pollution in Taiwan and its relation to hydro-meteorological factors

To better understand air pollution problems, the relationships between PM2.5 and hydro-meteorological variables are studied using a state-of-the-art multivariate nonlinear and non-stationary filtering method, noise-assisted multivariate empirical mode decomposition (NAMEMD), and the time-dependent intrinsic correlation (TDIC) algorithm. Three characteristic scales (annual, diurnal and semi-diurnal) are shown to be significant to PM2.5 characterization, based on using NAMEMD filtering. Temporal fluctuations of local correlations among PM2.5 and hydro-meteorological variables are presented. On diurnal and semi-diurnal scales, seasonal variation of the local correlation between temperature and humidity is observed. A combined wind speed and direction analysis can be conducted using the NAMEMD-based algorithm. The pollutant roses that are generated from the reconstructed wind directions reveal the sources of PM2.5 on different scales. PM2.5 is found to be related to land breeze at the diurnal scale and to winter monsoons at the annual scale. The scale-dependent wind direction that contributes to the increase of PM2.5 can be identified.