Mass extinction efficiency and extinction hygroscopicity of ambient PM2.5 in urban China

A review of quantification methods for light absorption enhancement of black carbon aerosol

Black carbon (BC) is a distinct type of carbonaceous aerosol that has a significant impact on the environment, human health, and climate. A non-BC material coating on BC can alter the mixing state of the BC particles, which considerably enhances the mass absorption efficiency of BC by directing more energy toward the BC cores (lensing effect). A lot of methods have been reported for quantifying the enhancement factor (Eabs), with diverse results. However, to the best of our knowledge, a comprehensive review specific to the quantification methods for Eabs has not been systematically performed, which is unfavorable for the evaluation of obtained results and subsequent radiative forcing. In this review, quantification methods are divided into two broad categories, direct and indirect, depending on whether experimental removal of the coating layer from an aged carbonaceous particle is required. The direct methods described include thermal peeling, solvent dissolution, and optical virtual exfoliation, while the indirect methods include intercept-linear regression fitting, minimum R squared, numerical simulation, and empirical value. We summarized the principles, procedures, virtues, and limitations of the major Eabs quantification methods and analyzed the current problems in the determination of Eabs. We pointed out what breakthroughs are needed to improve or innovate Eabs quantification methods, particularly regarding the need to avoid the influence of brown carbon, develop a broadband Eabs quantification scheme, quantify the Eabs values for the emissions of low-efficiency combustions, measure the Eabs of particles in a high-humidity environment, design a real-time monitor of Eabs by a proper combination of mature techniques, and make more use of artificial intelligence for better Eabs quantification. This review deepens the understanding of Eabs quantification methods and benefits the estimation of the contribution of BC to radiative forcing using climate models.

Hindered visibility improvement despite marked reduction in anthropogenic emissions in a megacity of southwestern China: An interplay between enhanced secondary inorganics formation and hygroscopic growth at prevailing high RH conditions

The PM2.5-bound visibility improvement remains challenging in China despite vigorous control on anthropogenic emissions in recent years. One critical issue could exist in the distinct physicochemical properties especially of secondary aerosol components. Taken the COVID-19 lockdown as an extreme case, we focus on the relationship between visibility, emission cuts, and secondary formation of inorganics with changing optical and hygroscopic behaviors in Chongqing, a representative city characterized with humid weather and poor diffusion conditions in Sichuan Basin, southwest of China. It is found that the increased secondary aerosol abundance (e.g., PM2.5/CO and PM2.5/PM10 as a proxy) with enhanced atmospheric oxidative capacity (e.g., O3/Ox, Ox = O3 + NO2), combined with insignificant meteorological dilution effect, might partly offset the benefit on the improved visibility from substantial reduction in anthropogenic emissions during the COVID-19 lockdown. This is in line with the efficient oxidation rates of sulfur and nitrogen (i.e., SOR, NOR), increasing more significantly with PM2.5 and relative humidity (RH) in comparison to O3/Ox. The resulted larger fraction of nitrate and sulfate (i.e., fSNA) would promote the optical enhancement (i.e., f(RH)) and mass extinction efficiency (MEE) of PM2.5, especially under highly humid conditions (e.g., RH > 80 %, with approximately half of the occurrence frequency). This could further facilitate secondary aerosol formation via aqueous-phase reaction and heterogeneous oxidation, likely due to enhanced water uptake and enlarged size/surface area upon hydration. In combination of gradually increased atmospheric oxidative capacity, this positive feedback would in turn inhibit the visibility improvement particularly at high RH environment. Considering the current air pollution complex status over China, further work on the formation mechanisms of major secondary species (e.g., sulfate, nitrate, and secondary organics), size-resolved chemical and hygroscopic properties, together with their interactions are highly recommended. Our results are hoping to assist in the atmospheric pollution complex mitigation and prevention in China.

Embedded information of aerosol type, hygroscopicity and scattering enhancement factor revealed by the relationship between PM2.5 and aerosol optical depth

Satellite aerosol optical depth (AOD) provides an alternative way to depict the spatial distribution of near-surface PM_2.5. In this study, a mathematical formulation of how PM_2.5 is related to AOD is presented. When simplified to a linear equation, a functional dependence of the slope on the aerosol type, scattering enhancement factor f(RH), and boundary layer height is revealed, while the influence of the vertical aerosol profile is embedded in the intercept. Specifically, we focus on the effects of aerosol properties and employ a new aerosol index (Normalized Gradient Aerosol Index, NGAI) for classifying aerosol subtypes. The combination of AOD difference at shorter wavelengths over longer-wavelength AOD from AERONET data could distinguish and subclassify aerosol types previously indistinguishable by AE (i.e., urban-industrial pollution, U/I, and biomass burning, BB). AOD-PM_2.5 regressions are performed on these aerosol subtypes at various relative humidity (RH) levels. The results suggest that BB aerosols are nearly hydrophobic until the RH exceeds 80 %, while the AOD-PM_2.5 regressions for U/I depend on RH levels. Moreover, the scattering enhancement factor f(RH) can be calculated by taking the ratio of intercepts between dry and humidity conditions, which is proposed and tested for the first time in this study. Our results show an f(RH ≥ 80 %) of ∼2.6 for U/I-dominated aerosols, whereas the value is not over 1.5 for BB aerosols. The f(RH) can be further used to derive the optical hygroscopicity parameter (κ_sca), demonstrating that the NGAI can be used to exploit differences in aerosol hygroscopicity and improve the AOD-PM_2.5 relationship.

Secondary inorganic aerosol chemistry and its impact on atmospheric visibility over an ammonia-rich urban area in Central Taiwan

This study investigated the hourly inorganic aerosol chemistry and its impact on atmospheric visibility over an urban area in Central Taiwan, by relying on measurements of aerosol light extinction, inorganic gases, and PM_2.5 water-soluble ions (WSIs), and simulations from a thermodynamic equilibrium model. On average, the sulfate (SO₄^2-), nitrate (NO₃^-), and ammonium (NH₄⁺) components (SNA) contributed ∼90% of WSI concentrations, which in turn made up about 50% of the PM_2.5 mass. During the entire observation period, PM_2.5 and SNA concentrations, aerosol pH, aerosol liquid water content (ALWC), and sulfur and nitrogen conversion ratios all increased with decreasing visibility. In particular, the NO₃^- contribution to PM_2.5 increased, whereas the SO₄^2- contribution decreased, with decreasing visibility. The diurnal variations of the above parameters indicate that the interaction and likely mutual promotion between NO₃^- and ALWC enhanced the hygroscopicity and aqueous-phase reactions conducive for NO₃^- formation, thus led to severely impaired visibility. The high relative humidity (RH) at the study area (average 70.7%) was a necessary but not sole factor leading to enhanced NO₃^- formation, which was more directly associated with elevated ALWC and aerosol pH. Simulations from the thermodynamic model depict that the inorganic aerosol system in the study area was characterized by fully neutralized SO₄^2- (i.e. a saturated factor in visibility reduction) and excess NH₄⁺ amidst a NH₃-rich environment. As a result, PM_2.5 composition was most sensitive to gas-phase HNO₃, and hence NOx, and relatively insensitive to NH₃. Consequently, a reduction of NOx would result in instantaneous cuts of NO₃^-, PM_2.5, and ALWC, and hence improved visibility. On the other hand, a substantial amount of NH₃ reduction (>70%) would be required to lower the aerosol pH, driving more than 50% of the particulate phase NO₃^- to the gas phase, thereby making NH₃ a limiting factor in shifting PM_2.5 composition.

A Study on the Long-Term Variations in Mass Extinction Efficiency Using Visibility Data in South Korea

Fine particulate matter (PM) release is regulated by environmental policies in most countries. This study investigated long–term trends in the mass extinction efficiency (Qe) of aerosols in Northeast Asia. For this purpose, the Qe was calculated using visibility, PM2.5 recorded between 2015 and 2020, and PM10 recorded between 2001 and 2020 at eight Korean sites. The Qe of PM10 (Qe,10) showed an increasing trend with 0.06~0.22 (m2/g)/yr in seven cities except for Jeju. The Qe of PM2.5 (Qe,2.5) also showed an increasing trend with 0.28–2.47 (m2/g)/yr in all cities. In this study, PM10 and PM2.5, were divided into low, moderate, and high concentrations, and the Qe value change by year was examined. Qe,10 showed a tendency to decrease at low concentrations (19–21 μg/m3). However, at moderate (69–71 μg/m3) and high concentrations (139–141 μg/m3), Qe,10 increased in most regions. Qe,2.5 showed an increasing trend at low concentration (9–11 μg/m3), moderate concentration (29–31 μg/m3), and high concentration (69–71 μg/m3), except for Suwon and Pohang, where data were insufficient for analysis. Both Qe,10 and Qe,2.5 showed an increasing trend. The increase in Qe indicated that the visibility-impairing effect of PM can increase even if the same concentration of PM is present. The visibility-impairing effects of PM vary based on the composition, size and other characteristics of the particles in the atmosphere at a given point in time and not simply the quantity of particles. This means that reducing the quantity of particles does not reliably produce a proportionate improvement in visibility. Air quality policies must take the variable nature of PM particles and their effect on visibility into account so that more consistent improvements in air quality can be achieved.

Estimation of Aerosol Extinction Coefficient Using Camera Images and Application in Mass Extinction Efficiency Retrieval

In this study, we attempted to calculate the extinction parameters of PM2.5 using images from a commercial camera. The photo pixels provided information on the characteristics of the objects (i.e., the reflectivity, transmittance, or extinction efficiency) and ambient brightness. Using the RGB values of pixels, we calculated the extinction coefficient and efficiency applied to the mass concentration of PM2.5. The calculated extinction coefficient of PM2.5 determined from the camera images had a higher correlation with the PM2.5 mass concentration (R2 = 0.7) than with the visibility data, despite the limited mass range. Finally, we identified that the method of calculating extinction parameters using the effective wavelength of RGB images could be applied to studies of changes in the atmosphere and aerosol characteristics. The mass extinction efficiency of PM2.5, derived from images, and the mass concentration of PM2.5 was (10.8 ± 6.9) m2 g−1, which was higher than the values obtained in Northeast Asia by previous studies. We also confirmed that the dry extinction efficiency of PM2.5, applied with a DRH of 40%, was reduced to (6.9 ± 5.0) m2 g−1. The extinction efficiencies of PM2.5, calculated in this study, were higher than those reported in previous other studies. We inferred that high extinction efficiency is related to changes in size or the composition of aerosols; therefore, an additional long-term study must be conducted.

Characterization of haze pollution in Zibo, China: Temporal series, secondary species formation, and PMx distribution

An online field observation was conducted in Zibo, China from September 1, 2018 to February 28, 2019, covering autumn and winter. Within the investigation period, the mean mass concentrations of PM₁, PM_2.5, and PM₁₀ were 49.3, 86.1, and 136.5 μg m^-3, respectively. OA (organic aerosol) was the most dominant species in PM_2.5 (39.7 %), followed by NO₃^- (26.3 %) and SO₄^2- (17.0 %), indicating the importance of secondary species on PM_2.5. Increase of particles were always accompanied increasing relative humidity (RH), slow wind, and increasing precursors, contributing the secondary transition. SO₄^2- was more susceptible to RH, indicating the dominant role of heterogeneous processes in its secondary formation. As RH increased, its strengthening effect on SO₄^2- increased as well. Photochemistry was the main contributor to the secondary formation of NO₃^-. The morning and evening rush hours determined the peak of absolute NO₃^- throughout the day. By classifying particles into three bins, we found that smaller particles were the biggest contributors (larger PM₁/PM_2.5) of slight pollution (35 < PM_2.5<115 μg m^-3). When severe haze occurred, PM_2.5 contributed more than particles of other sizes (PM₁ or PM₁₀). Secondary species contributed more to particles within 2.5 μm but less to larger particles. PM₁/PM_2.5 was high from 9:00 to 15:00, indicating the strong effect of photochemistry on smaller particles. In comparison, larger particles favored more humid conditions. NO₃^- preferentially existed in larger particles because the hygroscopicity of preexisting species (SO₄^2- and NO₃^-) promoted partitioning. SO₄^2- appeared a stable diurnal variation, replying its stable contribution to particles of different sizes.

Visibility Prediction over South Korea Based on Random Forest

In this study, the visibility of South Korea was predicted (VISRF) using a random forest (RF) model based on ground observation data from the Automated Synoptic Observing System (ASOS) and air pollutant data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) model. Visibility was predicted and evaluated using a training set for the period 2017–2018 and a test set for 2019. VISRF results were compared and analyzed using visibility data from the ASOS (VISASOS) and the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (VISLDAPS) operated by the Korea Meteorological Administration (KMA). Bias, root mean square error (RMSE), and correlation coefficients (R) for the VISASOS and VISLDAPS datasets were 3.67 km, 6.12 km, and 0.36, respectively, compared to 0.14 km, 2.84 km, and 0.81, respectively, for the VISASOS and VISRF datasets. Based on these comparisons, the applied RF model offers significantly better predictive performance and more accurate visibility data (VISRF) than the currently available VISLDAPS outputs. This modeling approach can be implemented by authorities to accurately estimate visibility and thereby reduce accidents, risks to public health, and economic losses, as well as inform on urban development policies and environmental regulations.

A novel algorithm to determine the scattering coefficient of ambient organic aerosols

In the present work, we propose a novel algorithm to determine the scattering coefficient of OA by evaluating the relationships of the MSEs for primary organic aerosol (POA) and secondary organic aerosol (SOA) with their mass concentrations at three distinct sites, i.e. an urban site, a rural site, and a background site in China. Our results showed that the MSEs for POA and SOA increased rapidly as a function of mass concentration in low mass loading. While the increasing rate declined after a threshold of mass loading of 50 μg/m³ for POA, and 15 μg/m³ for SOA, respectively. The dry scattering coefficients of submicron particles (PM₁) were reconstructed based on the algorithm for POA and SOA scattering coefficient and further verified by using multi-site data. The calculated dry scattering coefficients using our reconstructing algorithm have good consistency with the measured ones, with the high correlation and small deviation in Shanghai (R² = 0.98; deviations: 2.9%) and Dezhou (R² = 0.90; deviations: 4.7%), indicating that our algorithms for OA and PM₁ are applicable to predict the scattering coefficient of OA and Submicron particle (PM₁) in China.

Pollution severity-dependent aerosol light scattering enhanced by inorganic species formation in Beijing haze

The dependence of aerosol optical properties on the chemical composition and size of particles in haze in Beijing was studied. We measured the scattering coefficient of dehydrated PM_2.5 aerosols (σ_{sp_dry}) and analyzed the chemical composition of PM_2.5. We also monitored the size distribution of particles in the range of ~10-700 nm to observe the particle growth (PG_size). Results showed that the concentrations of secondary inorganic aerosols (SIAs) and the mean size of PM_2.5 particles (scattering Ångström exponent decreasing) increased with the deterioration of the air quality and increase in relative humidity (RH) which enhanced mass scattering efficiency and increased PM_2.5. Thus, the increase in σ_{sp_dry} was particularly dramatic and highly sensitive to the ambient RH in severe haze stages. When the ratio of SIAs to PM_2.5 (M_SIAs) exceeded 0.35 during the polluted environment, the water content, PG_size, and σ_{sp_dry} showed distinct increases, indicating that the formation of SIAs enhanced water vapor condensation and particle growth. This finding revealed the existence of a critical value for M_SIAs in terms of describing the correlation of σ_{sp_dry} variation with pollution severity. The estimation of the respective contributions of individual components to σ_{sp_dry} with the IMPROVE formula revealed that ammonium nitrate and ammonium sulfate were the two largest contributors. These results indicate that the rapid formation of SIAs and PG_size under humid conditions are the key factors contributing to the increased σ_{sp_dry} via enhanced mass scattering efficiency and increased PM_2.5 in the severe haze observed in this study.

Spatial Assessment of Health Economic Losses from Exposure to Ambient Pollutants in China

Increasing emissions of ambient pollutants have caused considerable air pollution and negative health impact for human in various regions of China over the past decade. The resulting premature mortality and excessive morbidity caused huge human economic losses to the entire society. To identify the differences of health economic losses in various regions of China and help decision-making on targeting pollutants control, spatial assessment of health economic losses due to ambient pollutants in China is indispensable. In this study, to better represent the spatial variability, the satellite-based retrievals of the concentrations of various pollutants (PM10, PM2.5, O3, NO2, SO2 and CO) for the time period from 2007 to 2017 in China are used instead of using in-situ data. Population raster data were applied together with exposure-response function to calculate the spatial distribution of health impact and then the health impact is further quantified by using amended human capital (AHC) approach. The results which presented in a spatial resolution of 0.25° × 0.25°, show the signification contribution from the spatial assessment to revealing the spatial distribution and variance of health economic losses in various regions of China. Spatial assessment of overall health economic losses is different from conventional result due to more detail spatial information. This spatial assessment approach also provides an alternative method for losses measurement in other fields.

AMPK activation attenuates inflammatory response to reduce ambient PM2.5-induced metabolic disorders in healthy and diabetic mice

Epidemiological and experimental studies have indicated that ambient fine particulate matter (PM_2.5) exposure is associated with the occurrence and development of metabolic disorders such as obesity and type 2 diabetes mellitus (T2DM). However, the mechanism is not clear yet, and there are few studies to explore the possible prevention measure. In this study, C57BL/6 and db/db mice were exposed to concentrated PM_2.5 or filtered air using Shanghai Meteorological and Environmental Animal Exposure System (Shanghai-METAS) for 12 weeks. From week 11, some of the mice were assigned to receive a subcutaneous injection of AMPK activator (AICAR). Lipid metabolism, glucose tolerance, insulin sensitivity and energy homeostasis were measured. Meanwhile, the respiratory, systemic and visceral fat inflammatory response was detected. The results showed that PM_2.5 exposure induced the impairments of glucose tolerance, insulin resistance, lipid metabolism disorders and disturbances of energy metabolism in both C57BL/6 and db/db mice. These impairments might be consistent with the increased respiratory, circulating and visceral adipose tissue (VAT) inflammatory response, which was characterized by the release of IL-6 and TNF-α in lung, serum and VAT. More importantly, AICAR administration led to the significant enhancement of energy metabolism, elevation of AMPK as well as the decreased IL-6 and TNF-α in VAT of PM_2.5-exposed mice, which suggesting that AMPK activation might attenuate the inflammatory responses in VAT via the inhibition of MAPKs and NFκB. The study indicated that exposure to ambient PM_2.5 under the concentration which is often seen in some developing countries could induce the occurrence of metabolic disorders in normal healthy mice and exacerbate metabolic disorders in diabetic mice. The adverse impacts of PM_2.5 on insulin sensitivity, energy homeostasis, lipid metabolism and inflammatory response were associated with AMPK inhibition. AMPK activation might inhibit PM_2.5-induced metabolic disorders via inhibition of inflammatory cytokines release. These findings suggested that AMPK activation is a potential therapy to prevent some of the metabolic disorders attributable to air pollution exposure.

Estimation of Source-Based Aerosol Optical Properties for Polydisperse Aerosols from Receptor Models

We estimated source-based aerosol optical properties for polydisperse aerosols according to a chemical-species-resolved mass contribution method based on source apportionment. We investigated the sensitivity of aerosol optical properties based on PM2.5 (particulate matter that have a diameter of less than 2.5 micrometers) monitoring results. These aerosols were composed of ions, metals, elemental carbon, and water-soluble organic carbon which includes humic-like carbon substances and water-soluble organic carbon. We calculated aerosols’ extinction coefficients based on the PM2.5 composition data and the results of a multivariate receptor model (Solver for Mixture Problem model, SMP). Based on the mass concentration of chemical composition and nine sources calculated with the SMP receptor model, we estimated the size-resolved mass extinction efficiencies for each aerosol source using a multilinear regression model. Consequently, this study quantitatively determined the size resolved sources contributing to the apportionment-based aerosol optical properties and calculated their respective contributions. The results show that source-resolved mass concentrations and extinction coefficients had varying contributions. This discrepancy between the source-based mass concentration and extinction coefficient was mainly due to differences between the source-dependent aerosol size distribution and the aerosol optical properties from different sources.

Ground-Level PM2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm

Particulate matter (PM) has a substantial influence on the environment, climate change and public health. Due to the limited spatial coverage of a ground-level PM2.5 monitoring system, the ground-based PM2.5 concentration measurement is insufficient in many circumstances. In this paper, a Specific Particle Swarm Extinction Mass Conversion Algorithm (SPSEMCA) using remotely sensed data is introduced. Ground-level observed PM2.5, planetary boundary layer height (PBLH) and relative humidity (RH) reanalyzed by the European Centre for Medium-Range Weather Forecasts (ECMWF) and aerosol optical depth (AOD), fine-mode fraction (FMF), particle size distribution, and refractive indices from AERONET (Aerosol Robotic Network) of the Beijing area in 2015 were used to establish this algorithm, and the same datasets for 2016 were used to test the performance of the SPSEMCA. The SPSEMCA involves four steps to obtain PM2.5 values from AOD datasets, and every step has certain advantages: (I) In the particle correction, we use η2.5 (the extinction fraction caused by particles with a diameter less than 2.5 μm) to make an accurate assimilation of AOD2.5, which is contributed to by the specific particle swarm PM2.5. (II) In the vertical correction, we compare the performance of PBLHc retrieved by satellite Lidar CALIPSO data and PBLHe reanalysis by ECMWF. Then, PBLHc is used to make a systematic correction for PBLHe. (III) For extinction to volume conversion, the relative humidity and the FMF are used together to assimilate the AVEC (averaged volume extinction coefficient, μm2/μm3). (IV) PM2.5 measured by ground-based air quality stations are used as the dry mass concentration when calculating the AMV (averaged mass volume, cm3/g) in humidity correction, that will avoid the uncertainties derived from the estimation of the particulate matter density ρ. (V) Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 km × 1 km AOD was used to retrieve high resolution PM2.5, and a LookUP Table-based Spectral Deconvolution Algorithm (LUT-SDA) FMF was used to avoid the large uncertainties caused by the MODIS FMF product. The validation of PM2.5 from the SPSEMCA algorithm to the AERONET observation data and MODIS monitoring data achieved acceptable results, R = 0.70, RMSE (root mean square error) = 58.75 μg/m3 for AERONET data, R = 0.75, RMSE = 43.38 μg/m3 for MODIS data, respectively. Furthermore, the trend of the temporal and spatial distribution of Beijing was revealed.

Case study of spring haze in Beijing: Characteristics, formation processes, secondary transition, and regional transportation

Continuous haze monitoring was conducted from 12:00 3 April to 12:00 8 April 2016 in Beijing, China to develop a more detailed understanding of spring haze characteristics. The PM_2.5 concentration ranged from 6.30 to 165 μg m^-3 with an average of 63.8 μg m^-3. Nitrate was the most abundant species, accounting for 36.4% of PM_2.5, followed by organic carbon (21.5%), NH₄⁺ (19.3%), SO₄^2- (18.8%), and elemental carbon (4.10%), indicating the key role of nitrate in this haze event. Species contribution varied based on the phase of the haze event. For example, sulfate concentration was high during the haze formation phase, nitrate was high during the haze, and secondary organic carbon (SOC) had the highest contribution during the scavenging phase. The secondary transition of sulfate was influenced by SO₂, followed by relative humidity (RH) and O_x (O₃+NO₂). Nitrate formation occurred in two stages: through NO₂ oxidation, which was vulnerable to O_x; and by the partitioning of N (+5) which was susceptible to RH and temperature. SOC tended to form when O_x and RH were balanced. According to hourly species behavior, sulfate and nitrate were enriched during haze formation when the mixed layer height decreased. However, SOC accumulated prior to the haze event and during formation, which demonstrated the strong contribution of secondary inorganic aerosols, and the limiting contribution of SOC to this haze case. Investigating backward trajectories showed that high speed northwestern air masses following a straight path corresponded to the clear periods, while southwesterly air masses which traversed heavily polluted regions brought abundant pollutants to Beijing and stimulated the occurrence of haze pollution. Results indicate that the control of NO₂ needs to be addressed to reduce spring haze. Finally, the correlation between air mass trajectories and pollution conditions in Beijing reinforce the necessity of inter-regional cooperation and control.

Insights into extinction evolution during extreme low visibility events: Case study of Shanghai, China

Apportionment of ambient extinction coefficient is essential for quantifying the causes of visibility degradation. Previous studies focused on either seasonal or episode-average extinction coefficients. The extinction evolution during different types of low visibility events was still unclear and seldom investigated. In this study, hourly-resolution apportionment of ambient extinction coefficient, including dry extinction coefficient and hygroscopic portion, during three low visibility events (i.e., dust storm, autumn and winter haze) and one clear episode was retrieved through online measurement in Shanghai, China. PM_2.5 soil and coarse particles contributed 90% of PM₁₀ mass and 62% of total extinction coefficient throughout the dust storm event. Secondary inorganic aerosol and organic matter dominated the autumn and winter haze events, accounting for 52% and 31% of PM_2.5 mass, 35% and 27% of extinction coefficient, respectively. Hygroscopic enhancement by inorganic particles contributed another 22-27% of extinction coefficient during the two haze events. However, higher relative humidity elevated the extinction percentage of inorganic aerosol and hygroscopic enhancement during the autumn haze, and the percentage of organic matter decreased correspondingly. In contrast, the extinction of each contributor increased proportionally and the percentages could keep at a stable level during the winter haze. Furthermore, the mass extinction efficiency of major PM_2.5 chemical components was found to increase with the accumulation of mass loading. These findings indicated the importance of reducing the mass level of organic matter and secondary inorganic aerosol during the autumn or winter haze events. The control of precursors of sulfur and nitrogen oxides seemed more effective for visibility improvement during the autumn events with higher relative humidity.