Simulation of a Severe Sand and Dust Storm Event in March 2021 in Northern China: Dust Emission Schemes Comparison and the Role of Gusty Wind

Northern China experienced a severe sand and dust storm (SDS) on 14/15 March 2021. It was difficult to simulate this severe SDS event accurately. This study compared the performances of three dust-emission schemes on simulating PM10 concentration during this SDS event by implementing three vertical dust flux parameterizations in the Comprehensive Air-Quality Model with Extensions (CAMx) model. Additionally, a statistical gusty-wind model was implemented in the dust-emission scheme, and it was used to quantify the gusty-wind contribution to dust emissions and peak PM10 concentration. As a result, the LS scheme (Lu and Shao 1999) produced the minimum errors for peak PM10 concentrations, the MB scheme (Marticorena and Bergametti 1995) underestimated the PM10 concentrations by 70–90%, and the KOK scheme (Kok et al. 2014) overestimated PM10 concentrations by 10–50% in most areas. The gusty-wind model could reasonably reproduce the probability density function of 2-min wind speeds. There were 5–40% more dust-emission flux and 5–40% more peak PM10 concentrations generated by the gusty wind than the hourly wind in the dust-source regions. The increase of peak PM10 concentration caused by gusty wind in the non-dust-source regions was higher than in the dust-source regions, with 10–50%. Implementing the gusty-wind model could help improve the LS scheme’s performance in simulating PM10 concentrations of this severe SDS event. More work is still needed to investigate the reliability of the gusty-wind model and LS scheme on various SDS events.

Analysis of Meteorological Conditions on Riverbed Dust Aerosol in Taiwan

Extremely high concentrations of dust particles are occasionally generated from the riverbeds of Taiwan, affecting the visibility and traffic safety of the local and nearby areas. The condition is most severe during the winter monsoon when surface wind is strong. This study analyzes the concentration of particulate matter of 10 µm or less (PM10), wind direction, wind speed, temperature, and humidity of riverbed stations adjacent to the Daan, Dajia, Dadu, Zhuoshui, and Beinan Rivers in Taiwan for a period of two years. The weather conditions that cause the high concentration of PM10 are classified into typhoon and non-typhoon types, and the latter type is further classified into three stages: ahead of front, ahead of anticyclone, and behind anticyclone. The associated meteorological influences of these weather types on high-concentration events in the riverbed are explored. The monitoring data show that the hourly PM10 concentration of the four riverbed stations exceeded 125 µg m−3 for 35–465 h per year, and the maximum PM10 in the Daan (and Dajia), and Zhuoshui Rivers was more than 800 µg m−3. Weather analysis showed that the extreme PM10 concentration on the riverbed was caused by weather types: typhoon and ahead of anticyclone, in which the peak hourly concentration reached average values of more than 600 and 400 µg m−3, respectively. The high PM10 caused by the typhoon type mainly occurred in October, with an average wind speed of 6 m s−1, high temperature of 25 °C, and mostly northeasterly winds. The ahead of anticyclone type mainly occurred in December, with an average wind speed of 5 m s−1, and northeasterly and northwesterly winds. Both weather types of riverbed events were observed during the daytime, especially at noon time, when strong wind speed, high temperature, and low relative humidity is favorable for riverbed dust generation. On the other hand, the main months of the high PM10 concentrations of the ahead of front and behind anticyclone stages are February and April. The peak PM10 concentrations of these two types of riverbed events are both about 300 µg m−3, but sporadic riverbed dust in these weather stages is mixed with Asian dust or pollution transported to the rivers through weak northwesterly and northeasterly winds. The high concentrations of these two types of riverbed events can occur at any time; but for the Dadu River, the high concentrations are often observed in the morning, when land breezes from the southeast bring local pollutants to the river.

Spatial and Temporal Variations in PM10 Concentrations between 2010–2017 in South Africa

Particulate matter less than or equal to 10 μm in aerodynamic diameter (PM10 µg/m3) is a priority air pollutant and one of the most widely monitored ambient air pollutants in South Africa. This study analyzed PM10 from monitoring 44 sites across four provinces of South Africa (Gauteng, Mpumalanga, Western Cape and KwaZulu-Natal) and aimed to present spatial and temporal variation in the PM10 concentration across the provinces. In addition, potential influencing factors of PM10 variations around the three site categories (Residential, Industrial and Traffic) were explored. The spatial trend in daily PM10 concentration variation shows PM10 concentration can be 5.7 times higher than the revised 2021 World Health Organization annual PM10 air quality guideline of 15 µg/m3 in Gauteng province during the winter season. Temporally, the highest weekly PM10 concentrations of 51.4 µg/m3, 46.8 µg/m3, 29.1 µg/m3 and 25.1 µg/m3 at Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape Province were recorded during the weekdays. The study results suggest a decrease in the change of annual PM10 levels at sites in Gauteng and Mpumalanga Provinces. An increased change in annual PM10 levels was reported at most sites in Western Cape and KwaZulu-Natal.

Haze Prediction Model Using Deep Recurrent Neural Network

In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes.

Analysis PM10 and Visibility During Forest Fire in Palangka Raya

Forest fires have an impact on air quality and visibility. Visibility can be associated with a highly visual indicator of air pollution. This research aims to analyze the relationship between the PM10 concentration and visibility during the forest firest events and normal conditions in Palangkaraya from 2000 to 2014 by using a regression method. The relative humidity data was used to filter the PM10 and visibility. Furthermore, the equation resulted from the regression analysis was used to predict PM10 concentration in Palangka Raya. The result showed that the regression pattern tends to form a logarithmic function. Specifically, without filtering data, the coefficient correlation (r-value) during the forest fire events and normal conditions are 0.69 and 0.5, respectively. Meanwhile, a data filtering method gives a higher relationship between PM10 and visibility, with the r-value of 0.7 for the forest fire events and 0.68 for the normal condition. On the other hand, the prediction of PM10 concentration indicates a high bias value due to the other influenced factors that have not been included in this study.

A Wavenet-Based Virtual Sensor for PM10 Monitoring

In this work, a virtual sensor for PM10 concentration monitoring is presented. The sensor is based on wavenet models and uses daily mean NO2 concentration and meteorological variables (wind speed and rainfall) as input. The methodology has been applied to the reconstruction of PM10 levels measured from 14 monitoring stations in Lombardy region (Italy). This region, usually affected by high levels of PM10, is a challenging benchmarking area for the implemented sensors. Neverthless, the performances are good with relatively low bias and high correlation.