A Quantitative Modeling and Prediction Method for Sustained Rainfall-PM2.5 Removal Modes on a Micro-Temporal Scale

PM2.5 is unanimously considered to be an important indicator of air quality. Sustained rainfall is a kind of typical but complex rainfall process in southern China with an uncertain duration and intervals. During sustained rainfall, the variation of PM2.5 concentrations in hour-level time series is diverse and complex. However, existing analytical methods mainly examine overall removals at the annual/monthly time scale, missing a quantitative analysis mode that applies micro-scale time data to describe the removal phenomenon. In order to further achieve air quality prediction and prevention in the short term, it is necessary to analyze its micro-temporal removal effect for atmospheric environment quality forecasting. This paper proposed a quantitative modeling and prediction method for sustained rainfall-PM2.5 removal modes on a micro-temporal scale. Firstly, a set of quantitative modes for sustained rainfall-PM2.5 removal mode in a micro-temporal scale were constructed. Then, a mode-constrained prediction of the sustained rainfall-PM2.5 removal effect using the factorization machines (FM) was proposed to predict the future sustained rainfall removal effect. Moreover, the historical observation data of Nanjing city at an hourly scale from 2016 to January 2020 were used for mode modeling. Meanwhile, the whole 2020 year observation data were used for the sustained rainfall-PM2.5 removal phenomenon prediction. The experiment shows the reasonableness and effectiveness of the proposed method.

Download Full-text

Spatio-Temporal Variations of the PM2.5/PM10 Ratios and Its Application to Air Pollution Type Classification in China

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.692440 ◽

2021 ◽

Vol 9 ◽

Author(s):

Hao Fan ◽

Chuanfeng Zhao ◽

Yikun Yang ◽

Xingchuan Yang

Keyword(s):

Air Pollution ◽

Southern China ◽

Spatial Scales ◽

Mainland China ◽

Temporal Variations ◽

Average Concentration ◽

Environmental Research ◽

Observation Data ◽

Important Indicator ◽

Hu Line

Particulate Matter (PM) is an important indicator of the degree of air pollution. The PM type and the ratio of coarse and fine PM particles determine the ability to affect human health and atmospheric processes. Using the observation data across the country from 2015 to 2018, this study investigates the distribution and proportion of PM2.5 and PM10 at different temporal and spatial scales in mainland China; clarifies the PM2.5, PM10 and PM2.5/PM10 ratios interrelation; and classifies the dust, mixed, and anthropogenic type aerosol. It shows that the annual average concentration of PM2.5 and PM10 decreased by 10.55 and 8.78 μg m−3 in 4 years. PM2.5, PM10, and PM2.5/PM10 ratios show obvious while different seasonal variations. PM2.5 is high in winter and low in summer, while PM10 is high in winter and spring, and low in summer and autumn. Differently, the PM2.5/PM10 ratios are the highest in winter, and the lowest in spring. PM2.5/PM10 ratios show strong independence on PM2.5 and PM10, implying that it can provide extra information about the aerosol pollution such as aerosol type. A classification method about air pollution types is then further proposed based on probability distribution function (PDF) morphology of PM2.5/PM10 ratios. The results show that dust type mainly lies in the west of Hu-Line, mixed type pollution distributes near Hu-Line, and the anthropogenic type dominates over North China Plain and cities in southern China. The results provide insights into China’s future clean air policy making and environmental research.

Download Full-text

Examining the Economic and Environmental Impacts of COVID-19 Using Earth Observation Data

Remote Sensing ◽

10.3390/rs13010005 ◽

2020 ◽

Vol 13 (1) ◽

pp. 5

Author(s):

William Straka ◽

Shobha Kondragunta ◽

Zigang Wei ◽

Hai Zhang ◽

Steven D. Miller ◽

...

Keyword(s):

Air Quality ◽

Los Angeles ◽

Environmental Impacts ◽

Demographic Data ◽

The United States ◽

Earth Observation ◽

Observation Data ◽

Washington Dc ◽

Mobility Data ◽

Earth Observation Data

The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.

Download Full-text

Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

10.5194/acp-2017-182 ◽

2017 ◽

Cited By ~ 1

Author(s):

Jianlin Hu ◽

Xun Li ◽

Lin Huang ◽

Qi Ying ◽

Qiang Zhang ◽

...

Keyword(s):

Air Quality ◽

Health Effects ◽

Air Pollutants ◽

Emission Inventory ◽

Model Performance ◽

Observation Data ◽

Emission Inventories ◽

Air Quality Model ◽

Quality Model ◽

Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

Download Full-text

A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster

Royal Society Open Science ◽

10.1098/rsos.180889 ◽

2018 ◽

Vol 5 (9) ◽

pp. 180889 ◽

Cited By ~ 2

Author(s):

Zhengqiu Zhu ◽

Bin Chen ◽

Sihang Qiu ◽

Rongxiao Wang ◽

Yiping Wang ◽

...

Keyword(s):

Air Quality ◽

Measurement Data ◽

Monitoring Network ◽

Quality Monitoring ◽

Data Driven ◽

Atmospheric Environment ◽

Illustrative Case ◽

Air Quality Monitoring ◽

Detection Capability ◽

Data Driven Approach

The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

Download Full-text

Study on the Content and Influence of Heavy Metal Elements in Dust of Xiqing District of Tianjin

E3S Web of Conferences ◽

10.1051/e3sconf/202124801042 ◽

2021 ◽

Vol 248 ◽

pp. 01042

Author(s):

YanShan Yin ◽

Jin Xu

Keyword(s):

Heavy Metal ◽

Air Quality ◽

Human Health ◽

Inductively Coupled Plasma ◽

Great Influence ◽

Electric Heating ◽

Atmospheric Environment ◽

Total Dust ◽

Metal Elements ◽

Heavy Metal Elements

At present, heavy metal elements in dust have great influence on air quality and human health,therefore, the content and influence of heavy metal elements on campus were studied. Firstly, PM10 and total dust in the autumn campus atmosphere were sampled for 10 consecutive days, and then digested by electric heating plate digestion method. Then, inductively coupled plasma emission spectrometer (ICP-OES) was used to detect and analyze the content and concentration ratio of seven heavy metal elements Cu, Pb, Cd, Zn, Cr, Hg and Ba in PM10 and total dust. Finally, through comparative analysis, it is concluded that heavy metal pollutants in the atmospheric environment are mainly Zn and Ba, and the concentrations of Cd, Zn and Ba are seriously exceeded, so the air quality in Xiqing District of Tianjin is poor, and the particle size distribution of Cd and Hg makes it easy to enter the human body, which is especially unfavorable to human health.

Download Full-text

Research on air quality prediction method in Hangzhou based on machine learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/2010/1/012011 ◽

2021 ◽

Vol 2010 (1) ◽

pp. 012011

Author(s):

Zhongjie Fu ◽

Haiping Lin ◽

Bingqiang Huang ◽

Jiana Yao

Keyword(s):

Machine Learning ◽

Air Quality ◽

Prediction Method ◽

Quality Prediction ◽

Air Quality Prediction

Download Full-text

Underground operations in Internatsionalny mine rock mass susceptible to gas dynamic phenomena

Gornyi Zhurnal ◽

10.17580/gzh.2021.01.13 ◽

2021 ◽

pp. 76-80

Author(s):

A. A. Vyunikov ◽

◽

S. G. Vorozhtsov ◽

N. V. Khoyutanova ◽

E. K. Pul ◽

...

Keyword(s):

Rock Mass ◽

Prediction Method ◽

Test Site ◽

Gas Release ◽

Prevention Measures ◽

Science Center ◽

Gas Dynamic ◽

Prediction And Prevention ◽

Engineering Sciences ◽

Dynamic Phenomena

Starting from 2019 extraction of diamond ore reserves from Internatsionalnaya pipe by Internatsionalny mine of ALROSA is carried out in complex geological and geomechanical conditions. Geodynamic situation is complicated by a few gas dynamic phenomena of different nature and scale recorded. The investigation results on gas release dynamics from dolomite rock mass during drivage of Spiral decline, in a test site on level.-802/-820 m in case of prediction and prevention measures undertaken to combat gas dynamic phenomena are presented. The dynamic characteristics of gas in outburst-hazardous dolomite rocks are calculated by the initial velocity of gas release. Efficiency of the current outburst hazard prediction method using facility MIG-Ts1 is proved. At the same time, it is necessary to perform additional studies to collect sufficient statistics and reliable data on gas release and gas pressure during drivage operations in the mine. The authors appreciate participation of Deputy CEO of VostNII Science Center, Doctor of Engineering Sciences, Professor V. S. Zykov, Doctor of Engineering Sciences, Professor A. V. Dzhigrin, Director of Research Center for Applied Geomechanics and Convergent Technologies in Mining at NUST MISIS College of Mining, Doctor of Engineering Sciences, Professor of the Russian Academy of Sciences V. A. Eremenko and Director of VNIMI’s Kemerovo Division, Candidate of Engineering Sciences P. V. Grechishkin.

Download Full-text

Impact of the Christmas 2018 Mount Etna Eruption on the Regional Air Quality

10.5194/egusphere-egu21-2167 ◽

2021 ◽

Author(s):

Claire Lamotte ◽

Jonathan Guth ◽

Virginie Marécal ◽

Giuseppe Salerno ◽

Nicolas Theys ◽

...

Keyword(s):

Air Quality ◽

Regional Scale ◽

Volcanic Eruptions ◽

Mount Etna ◽

Surface Measurement ◽

Volcanic Plume ◽

Observation Data ◽

Sulfate Aerosols ◽

Lower Altitude ◽

The Impact

Volcanic eruptions are events that can eject several tons of material into the atmosphere. Among these emissions, sulfur dioxide is the main sulfurous volcanic gas. It can form sulfate aerosols that are harmful to health or, being highly soluble, it can condense in water particles and form acid rain. Thus, volcanic eruptions can have an environmental impact on a regional scale.The Mediterranean region is very interesting from this point of view because it is a densely populated region with a strong anthropogenic activity, therefore polluted, in which Mount Etna is also located. Mount Etna is the largest passive SO2 emitter in Europe, but it can also sporadically produce strong eruptive events. It is then likely that the additional input of sulfur compounds into the atmosphere by volcanic emissions may have effects on the regional atmospheric sulfur composition.We are particularly investigating the eruption of Mount Etna on December 24, 2018 [Corradini et al, 2020]. This eruption took place along a 2 km long breach on the side of the volcano, thus at a lower altitude than its main crater. About 100 kt of SO2 and 35 kt of ash were released in total, between December 24 and 30. With the exception of the 24th, the quantities of ash were always lower than the SO2.The availability of the TROPOMI SO2column estimates, at fine spatial resolution (7 km x 3.5 km at nadir) and associated averaging kernels, during this eruptive period made it also an excellent case study. It allows us to follow the evolution of SO2 in the volcanic plume over several days.Using the CNRM MOCAGE chemistry-transport model (CTM), we aim to quantify the impact of this volcanic eruption on atmospheric composition, sulfur deposition and air quality at the regional scale. The comparison of the model with the TROPOMI observation data allows us to assess the ability of the model to properly represent the plume. In spite of a particular meteorological situation, leading to a complex plume transport, MOCAGE shows a good agreement with TROPOMI observations. Thus, from the MOCAGE simulation, we can evaluate the impact of the eruption on the regional concentrations of SO2 and sulfate aerosols, but also analyse the quantities of dry and wet deposition, and compare it to surface measurement stations.

Download Full-text