Climate modulation of Niño3.4 SST-anomalies on air quality change in southern China: Application to seasonal forecast of haze pollution

2019 ◽  
Vol 225 ◽  
pp. 157-164 ◽  
Author(s):  
Xugeng Cheng ◽  
Richard Boiyo ◽  
Tianliang Zhao ◽  
Xiangde Xu ◽  
Sunling Gong ◽  
...  
Keyword(s):  
Air Quality ◽  
Southern China ◽  
Seasonal Forecast ◽  
Quality Change ◽  
Haze Pollution ◽  
Sst Anomalies

Diagnosing Sources of U.S. Seasonal Forecast Skill

2006 ◽  
Vol 19 (13) ◽  
pp. 3279-3293 ◽  
Author(s):  
X. Quan ◽  
M. Hoerling ◽  
J. Whitaker ◽  
G. Bates ◽  
T. Xu
Keyword(s):  
Air Temperature ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Diagnostic Methods ◽  
Sea Surface ◽  
Empirical Methods ◽  
Spatial Coverage ◽  
Sst Anomalies

Abstract In this study the authors diagnose the sources for the contiguous U.S. seasonal forecast skill that are related to sea surface temperature (SST) variations using a combination of dynamical and empirical methods. The dynamical methods include ensemble simulations with four atmospheric general circulation models (AGCMs) forced by observed monthly global SSTs from 1950 to 1999, and ensemble AGCM experiments forced by idealized SST anomalies. The empirical methods involve a suite of reductions of the AGCM simulations. These include uni- and multivariate regression models that encapsulate the simultaneous and one-season lag linear connections between seasonal mean tropical SST anomalies and U.S. precipitation and surface air temperature. Nearly all of the AGCM skill in U.S. precipitation and surface air temperature, arising from global SST influences, can be explained by a single degree of freedom in the tropical SST field—that associated with the linear atmospheric signal of El Niño–Southern Oscillation (ENSO). The results support previous findings regarding the preeminence of ENSO as a U.S. skill source. The diagnostic methods used here exposed another skill source that appeared to be of non-ENSO origins. In late autumn, when the AGCM simulation skill of U.S. temperatures peaked in absolute value and in spatial coverage, the majority of that originated from SST variability in the subtropical west Pacific Ocean and the South China Sea. Hindcast experiments were performed for 1950–99 that revealed most of the simulation skill of the U.S. seasonal climate to be recoverable at one-season lag. The skill attributable to the AGCMs was shown to achieve parity with that attributable to empirical models derived purely from observational data. The diagnostics promote the interpretation that only limited advances in U.S. seasonal prediction skill should be expected from methods seeking to capitalize on sea surface predictors alone, and that advances that may occur in future decades could be readily masked by inherent multidecadal fluctuations in skill of coupled ocean–atmosphere systems.

scholarly journals How Will Air Quality Change in South Asia by 2050?

2018 ◽  
Vol 123 (3) ◽  
pp. 1840-1864 ◽  
Author(s):  
Rajesh Kumar ◽  
Mary C. Barth ◽  
G. G. Pfister ◽  
L. Delle Monache ◽  
J. F. Lamarque ◽  
...  
Keyword(s):  
Air Quality ◽  
South Asia ◽  
Quality Change

scholarly journals Fast oxidation of sulfur dioxide by hydrogen peroxide in deliquesced aerosol particles

2020 ◽  
Vol 117 (3) ◽  
pp. 1354-1359 ◽  
Author(s):  
Tengyu Liu ◽  
Simon L. Clegg ◽  
Jonathan P. D. Abbatt
Keyword(s):  
Hydrogen Peroxide ◽  
Air Quality ◽  
Sulfur Dioxide ◽  
Atmospheric Aerosol ◽  
Flow Reactor ◽  
Aerosol Particles ◽  
Quality Models ◽  
Atmospheric Aerosol Particles ◽  
Haze Pollution ◽  
Sulfate Formation

Atmospheric sulfate aerosols have important impacts on air quality, climate, and human and ecosystem health. However, current air-quality models generally underestimate the rate of conversion of sulfur dioxide (SO2) to sulfate during severe haze pollution events, indicating that our understanding of sulfate formation chemistry is incomplete. This may arise because the air-quality models rely upon kinetics studies of SO2 oxidation conducted in dilute aqueous solutions, and not at the high solute strengths of atmospheric aerosol particles. Here, we utilize an aerosol flow reactor to perform direct investigation on the kinetics of aqueous oxidation of dissolved SO2 by hydrogen peroxide (H2O2) using pH-buffered, submicrometer, deliquesced aerosol particles at relative humidity of 73 to 90%. We find that the high solute strength of the aerosol particles significantly enhances the sulfate formation rate for the H2O2 oxidation pathway compared to the dilute solution. By taking these effects into account, our results indicate that the oxidation of SO2 by H2O2 in the liquid water present in atmospheric aerosol particles can contribute to the missing sulfate source during severe haze episodes.

Regional source apportionment of ozone and PM2.5 during the EXPLORE-YRD campaign in the Yangtze River Delta region of China

2020 ◽  
Author(s):  
Jianlin Hu ◽  
Lin Li ◽  
Jingyi Li ◽  
Xueying Wang ◽  
Kangjia Gong
Keyword(s):  
Air Quality ◽  
Yangtze River Delta ◽  
River Delta ◽  
Formation Mechanisms ◽  
Aerosol Formation ◽  
Air Quality Model ◽  
Quality Model ◽  
Haze Pollution ◽  
Source Category ◽  
The Yrd

<p>Although the air quality in China has been improved by collaborative efforts dedicating to mitigate the haze pollution, PM2.5 concentrations still remain high levels and the issue of increasing O<sub>3</sub> concentration has attracted more attention of the public. The YRD region has been suffering from both the PM2.5 and O3 pollution problems. To investigate the formation mechanisms and sources of PM2.5 and O3 in this region, a comprehensive EXPLORE-YRD campaign (EXPeriment on the eLucidation of theatmospheric Oxidation capacity and aerosol foRmation, and their Effects inYangtze River Delta) was carried out in May - June 2018. In this study, we investigate the contributions of different source categories to PM2.5 and O<sub>3</sub>. A source-oriented 3-D air quality model (CMAQ) was applied to analyze contributions of different emission sources to PM2.5 and O<sub>3 </sub>in the YRD region. Emissions were divided into eight source categories: industry, power, transportation, residential, agriculture, biogenic, wildfire, and other countries. Contribution from individual source category was quantified. The importance of anthropogenic and natural sources to PM2.5 and O<sub>3</sub> was discussed.</p>

Formation process of the widespread extreme haze pollution over northern China in January 2013: Implications for regional air quality and climate

2014 ◽  
Vol 98 ◽  
pp. 417-425 ◽  
Author(s):  
Minghui Tao ◽  
Liangfu Chen ◽  
Xiaozhen Xiong ◽  
Meigen Zhang ◽  
Pengfei Ma ◽  
...  
Keyword(s):  
Air Quality ◽  
Formation Process ◽  
Northern China ◽  
Haze Pollution ◽  
Regional Air Quality

Transport paths and vertical exchange characteristics of haze pollution in Southern China

2018 ◽  
Vol 625 ◽  
pp. 1074-1087 ◽  
Author(s):  
T.Y. Li ◽  
X.J. Deng ◽  
Y. Li ◽  
Y.S. Song ◽  
L.Y. Li ◽  
...  
Keyword(s):  
Southern China ◽  
Vertical Exchange ◽  
Haze Pollution

scholarly journals Impact of COVID-19 on Air Quality in Central and Eastern China

2021 ◽  
Author(s):  
Haixia Feng ◽  
Erwei Ning ◽  
Haiying Feng ◽  
Jian Li ◽  
Qi Wang
Keyword(s):  
Air Quality ◽  
Traffic Congestion ◽  
Quality Index ◽  
Southern China ◽  
Eastern China ◽  
Product Data ◽  
Epidemic Period ◽  
Abnormal Increase ◽  
The Impact ◽  
Traffic Behavior

Abstract The focus of this paper is mainly on COVID-19’s impact on the air quality in central and eastern China using MCD19A2 aerosol optical depth (AOD) product data as well as the impact of human activities (mainly traffic behavior) on air quality. The main conclusions are the following: Significant data are still missing in MCD19A2 AOD product data, which led to the abnormal increase of AOD in southern China in February and the decline of analysis accuracy in AOD and air quality; COVID-19 had the important impact on air quality index (AQI) and peak congestion delay index (PCDI), resulting in the precipitous decrease of AQI and PCDI in Q1 2020, and the peaks of the AQI during the epidemic period were almost closely related to people's activities. AQI, PM2.5, and NO2 was significantly positively correlated with PCDI. Therefore, the alleviation of traffic congestion plays an important role in improving the air quality.

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

2021 ◽  
Vol 13 (19) ◽  
pp. 11022
Author(s):  
Tingchen Wu ◽  
Xiao Xie ◽  
Bing Xue ◽  
Tao Liu
Keyword(s):  
Air Quality ◽  
Southern China ◽  
Prediction Method ◽  
Temporal Scale ◽  
Atmospheric Environment ◽  
Quantitative Modeling ◽  
Observation Data ◽  
Important Indicator ◽  
Modeling And Prediction ◽  
Prediction And Prevention

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.

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