The negative effects of PM2.5 concentration in urban development are becoming more and more prominent. Bernaola-Galvan Segmentation Algorithm (BGSA) and wavelet analysis are powerful tools for processing non-linear and non-stationary signals. First, we use BGSA that reveals there are 41 mutation points in the PM2.5 concentration in Guiyang. Then, we reveal the multi-scale evolution of PM2.5 concentration in Guiyang by wavelet analysis. In the first part, we performed one-dimensional continuous wavelet transform (CWT) on the eight monitoring points in the study area, and the results showed that they have obviously similar multi-scale evolution characteristics, with a high-energy and significant oscillation period of 190-512 days. Next, the wavelet transform coherence (WTC) reveals the mutual relationship between the PM2.5 concentration and the atmospheric pollutants and meteorological factors. PM2.5 concentration variation is closely linked to that of PM10 concentration. But, it is not to be ignored that the increase in the SO2 and NO2 concentrations will cause the PM2.5 concentration to rise on different scales. Lastly, the variation of the PM2.5 concentration can be better explained by the combination of multiple factors (2-4) using the multiple-wavelet coherence (MWC). Under the combination of the two factors, the average temperature (Avgtem) and relative humidity (ReH) have the highest AWC and PASC. In the case of the combination of four factors, CO-Avgtem-Wind-ReH plays the largest role in determining PM2.5 concentration.

Brown carbon (BrC) has been recognized as an important light-absorbing carbonaceous aerosol, yet understanding of its influence on regional climate and air quality has been lacking, mainly due to the ignorance of regional coupled meteorology-chemistry models. Besides, assumptions about its emissions in previous explorations might cause large uncertainties in estimates. Here, we implemented a BrC module into the WRF-Chem model that considers source-dependent absorption and avoids uncertainties caused by assumptions about emission intensities. To our best knowledge, we made the first effort to consider BrC in a regional coupled model. We then applied the developed model to explore the impacts of BrC absorption on radiative forcing, regional climate, and air quality in East Asia. We found notable increases in aerosol absorption optical depth (AAOD) in areas with high OC concentrations. The most intense forcing of BrC absorption occurs in autumn over Southeast Asia, and values could reach around 4 W m(-2). The intensified atmospheric absorption modified surface energy balance, resulting in subsequent declines in surface temperature, heat flux, boundary layer height, and turbulence exchanging rates. These changes in meteorological variables additionally modified near-surface dispersion and photochemical conditions, leading to changes of PM2.5 and O-3 concentrations. These findings indicate that BrC could exert important influence in specific regions and time periods. A more in-depth understanding could be achieved later with the developed model.

PM2.5 impacts the atmospheric temperature structure through scattering or absorbing solar radiation, whose concentration and composition can affect the impact. This study calculated the effect of PM2.5 on the temperature structures in the urban centre and the suburbs of Nanjing, as well as their differences. The results show that the optical parameters, atmospheric heating rate, radiative forcing, and temperature are all impacted by the concentration and composition of PM2.5. The uneven distribution of PM2.5 influences the differences in those factors between the urban centre and suburbs. In spring, summer, autumn, and winter, surface temperatures in the urban centre were approximately 283 K, 285 K, 305 K, and 277 K, while those in the suburbs were approximately 282 K, 283 K, 304 K, and 274 K. The urban heat island intensity has been reduced by 0.1-0.4 K due to the presence of PM2.5 in Nanjing. Due to the black carbon component's warming effect on the top of the boundary layer, the impact of PM2.5 on the urban heat island intensity profile drops quickly at the 0.75-1.25 km. PM2.5 may mask the warm city problem and have a more complex impact on the urban climate.

Air pollutants can be transported to the pristine regions such as the Tibetan Plateau, by monsoon and stratospheric intrusion. The Tibetan Plateau region has limited local anthropogenic emissions, while this region is influenced strongly by transport of heavy emissions mainly from South Asia. We conducted a comprehensive study on various air pollutants (PM2.5, total gaseous mercury, and surface ozone) at Nam Co Station in the inland Tibetan Plateau. Monthly mean PM2.5 concentration at Nam Co peaked in April before monsoon season, and decreased during the whole monsoon season (June-September). Monthly mean total gaseous mercury concentrations at Nam Co peaked in July and were in high levels during monsoon season. The Indian summer monsoon acted as a facilitator for transporting gaseous pollutants (total gaseous mercury) but a suppressor for particulate pollutants (PM2.5) during the monsoon season. Different from both PM2.5 and total gaseous mercury variabilities, surface ozone concentrations at Nam Co are primarily attributed to stratospheric intrusion of ozone and peaked in May. The effects of the Indian summer monsoon and stratospheric intrusion on air pollutants in the inland Tibetan Plateau are complex and require further studies. (C) 2021 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V.

To understand the characteristics of particulate matter (PM) and other air pollutants in Xinjiang, a region with one of the largest sand-shifting deserts in the world and significant natural dust emissions, the concentrations of six air pollutants monitored in 16 cities were analyzed for the period January 2013-June 2019. The annual mean PM2.5, PM10, SO2, NO2, CO, and O-3 concentrations ranged from 51.44 to 59.54 mu g m(-3), 128.43-155.28 mu g m(-3), 10.99-17.99 mu g m(-3), 26.27-31.71 mu g m(-3), 1.04-1.32 mg m(-3), and 55.27-65.26 mu g m(-3), respectively. The highest PM concentrations were recorded in cities surrounding the Taklimakan Desert during the spring season and caused by higher amounts of wind-blown dust from the desert. Coarse PM (PM10-2.5) was predominant, particularly during the spring and summer seasons. The highest PM2.5/PM10 ratio was recorded in most cities during the winter months, indicating the influence of anthropogenic emissions in winters. The annual mean PM2.5 (PM10) concentrations in the study area exceeded the annual mean guidelines recommended by the World Health Organization (WHO) by a factor of ca. similar to 5-6 (similar to 7-8). Very high ambient PM concentrations were recorded during March 19-22, 2019, that gradually influenced the air quality across four different cities, with daily mean PM2.5 (PM10) concentrations similar to 8-54 (similar to 26-115) times higher than the WHO guidelines for daily mean concentrations, and the daily mean coarse PM concentration reaching 4.4 mg m(-3). Such high PM2.5 and concentrations pose a significant risk to public health. These findings call for the formulation of various policies and action plans, including controlling the land degradation and desertification and reducing the concentrations of PM and other air pollutants in the region. (C) 2020 Elsevier Ltd. All rights reserved.

Fine particles (PM2.5) scatter and absorb solar radiation affecting the atmospheric temperature structure, and the effects vary with different concentrations and compositions. This study investigated the effect of PM2.5 on the urban temperature structure of Nanjing through concen- tration-and species-sensitive experiments using a box model. The results show that the optical parameters, atmospheric heating rate, radiative forcing, and temperature are affected by the PM2.5 concentration, PM2.5 composition, and relative humidity. Under 80% relative humidity, the asymmetry and single scattering albedo (SSA) were 0.7 and 0.88, while under 20% relative hu-midity, they were 0.6 and 0.77, respectively. PM2.5 increased the atmospheric heating rate by 1-18 K/day; while the surface temperature decreased with the presence of PM2.5. Furthermore, the heterogeneous concentration and composition distributions of PM2.5 led to changes in urban heat island (UHI) intensity. The UHI intensity could be reduced by 1-3 K by PM2.5, and the reduction increased with the increase in PM2.5 concentration and absorbing compositions. The existence of absorbing compositions and high concentrations of PM2.5 may work together to mask the UHI effect and other problems of urban development from 2000s till the present.

Ambient fine particulate matter (PM2.5) concentrations in India frequently exceed 100 mu g/m(3) during fall and winter pollution episodes. We use the GEOS-Chem chemical transport model with the TwO-Moment Aerosol Sectional microphysics scheme with 15 size bins (TOMAS15) to assess PM2.5 composition and impacts on radiation and cloud condensation nuclei (CCN) during pollution episodes as compared to the seasonal (October-December) average. We conduct high resolution (0.25 degrees x 0.3125 degrees) nested-domain simulations over India for short-duration, high-PM2.5 episodes in the fall of 2015 and 2017. The simulations capture the magnitude and spatial patterns of pollution episodes measured by surface monitors (r(PM2.5)(2) = 0.69) although aerosol optical depth is underestimated. During the episodes, near-surface organic matter (OM), black carbon (BC), and secondary inorganic aerosol concentrations increase from seasonal averages by up to 36, 7, and 7 mu g/m(3), respectively. Episodic aerosol increases enhance cooling by lowering the top-of-atmosphere clear-sky direct radiative effect (DRETOA) during the 2015 episode (-6 W/m(2)), with a smaller impact during the 2017 episode (-1 W/m(2)). Differences in DRETOA reflect larger increases in scattering aerosols in the column during the 2015 episode (+17 mg/m(2)) than in 2017 (+13 mg/m(2)), while absorbing aerosol column enhancements are smaller (+3 mg/m(2)) in both years. Changes in shortwave radiation at the surface (SWsfc) are spatially similar to DRETOA and mostly negative during both episodes. CCN enhancements (0.2% supersaturation) during these episodes occur across the western Indo-Gangetic Plain, coincident with higher PM2.5 concentrations. Changes in DRETOA, SWsfc, and CCN during high-PM2.5 episodes may have implications for crops, the hydrologic cycle, and surface temperature.

This study inspects the concentrations of fine particulate matter (PM2.5) mass and carbonaceous species, including organic carbon (OC) and elemental carbon (EC), as well as their thermal fractions in the Indian Himalayan glacier region at the western Himalayan region (WHR; Thajiwas glacier, 2799 m asl), central Himalayan region (CHR; Gomukh glacier, 3415 m asl), and eastern Himalayan region (EHR; Zemu glacier, 2700 m asl) sites, throughout the summer and winter periods of 2019-2020. Ambient PM2.5 samples were collected on quartz fiber filters using a low-volume sampler, followed by carbon (OC and EC) quantification using the IMPROVE_A thermal/optical reflectance methodology. Different seasonal variations in PM2.5 and carbonaceous species levels were found at all three sites investigated. Averaged PM2.5 mass ranged 55-87 mu g m-3 with a mean of 55.45 +/- 16.30 mu g m-3 at WHR, 86.80 +/- 35.73 mu g m-3 at CHR, and 72.61 +/- 24.45 mu g m-3 at EHR. Among the eight carbon fractions, high-temperature OC4 (evolved at 580 degrees C in the helium atmosphere) was the most prevalent carbon fraction, followed by low-temperature OC2 (280 degrees C) and EC1 (580 degrees C at 2% oxygen and 98% helium). Char-EC representing incomplete combustion contributed to 56, 67, and 53% of total EC, whereas soot EC contributed to 38, 26, and 43% of total EC in WHR, CHR, and EHR, respectively. The measured OC/EC ratios imply the presence of secondary organic carbon, whereas char-EC/soot-EC ratios suggested that biomass burning could be the predominant source of carbon at CHR, whereas coal combustion and vehicular emission might be dominant sources at WHR and EHR sites.

Given the advantages of remote sensing, an increasing number of satellite aerosol optical depths (AOD) have been utilized to evaluate near-ground PM2.5. However, the spatiotemporal relationship between AODs and PM2.5 still lacks a comprehensive investigation, especially in some regions with severe pollution within China. Here, we investigated the spatiotemporal relationships between several satellite AODs and the near-surface PM2.5 concentration across China and its 14 representative regions during 2016-2018 using the correlation coefficient (R), the PM2.5/AOD ratio (eta), the geo-detector (q), and the different aerosol-dominated regimes. The results showed that the MODIS AOD from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm strongly correlates with PM2.5 (R > 0.6) in China, particularly in the Chengyu (CY), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) regions. The close correlations (R = 0.7) exist between PM2.5 and MODIS and VIIRS AOD from the deep blue (DB) algorithm in the CY, BTH, and YRD regions. Under the key aerosols affecting China (e.g., sulfate and dust), there is a strong correlation (R > 0.5) between the PM2.5 and MODIS and VIIRS AODs from the MAIAC and DB algorithms, with the higher concentration of ground-level PM2.5 per unit of these AODs (eta > 130). The MAIAC AOD (Terra/Aqua) can better explain the spatial distribution (q > 0.4) of PM2.5 than those of AODs from the dark target (DT) and DB algorithms applied to the MODIS over China and its specific regions across seasons. The performance of the Advanced Himawari Imager (AHI) AOD (R > 0.5, q > 0.3) was close to that of the MAIAC AOD during the spring and summer; however, it was far less than the MAIAC AOD in the autumn and winter seasons. The investigation provides instructions for estimating the near-surface PM2.5 concentration based on AOD in different regions of China.

Black carbon (BC) aerosols have severe impacts on climate and health. Most atmospheric BC loadings are now predominantly reported for the PM2.5 size cut-off. Based on 39 published set of ambient BC concentrations from around the world where PM2.5 and PM10 were collected in parallel, we demonstrate that BC in PM2.5 was only around 80% of that in PM10. The implication is that around 20% of BC in the global ambient atmosphere is ignored with the now-legacy PM2.5 sampling approach. Correspondingly, BC of freshly emitted particles from combustion activities is dominantly reported in terms of PM2.5, and thus inflicting a bias in the total BC emission inventories. A consequence is that ambient BC is underpredicted when derived from models based on (PM2.5) emission inventories. This consideration contributes to reconcile existing systematic offset between model predictions and observation-based estimates of climate-relevant effects of anthropogenic BC aerosols. We propose that total ambient BC concentration should be considered rather than the PM2.5 portion to reduce the uncertainties in estimates of BC effects on the climate.