Short-lived dust storms (DS) pose significant challenges due to their sudden onset, rapid intensification, and severe impacts on air quality, visibility, transportation, and public health. Unlike long-duration dust storms, these short-duration events remain poorly understood, largely because of limitations in observations and forecasting capabilities. The dust storm that occurred over Delhi on 11 April 2025 provides a valuable case to examine the meteorological processes driving such rapid-onset events. ERA5 fields reveal the presence of a low-pressure system over northwest India, accompanied by strong westerly surface winds exceeding 12 m s⁻1, which facilitated dust transport from nearby arid regions. Radiosonde derived convective available potential energy shows a sharp increase ( 2300 J kg⁻1) on the dust storm day, compared to 700 J kg⁻1 on the preceding day, indicating a highly unstable atmosphere conducive to deep convection and dust uplift. HYSPLIT back-trajectory analysis further confirms the dust transport from source regions. AERONET and satellite observations show enhanced aerosol loading, with aerosol optical depth peaking at 0.73 and 0.6 during the event, respectively. An abrupt rise in PM₁₀ and PM₂.₅ concentrations is noticed 950 µg m⁻3 and 200 µg m⁻3, respectively, highlighting the dominance of coarse-mode dust aerosols. This abundance of dust aerosols reduced visibility from 3500 m to 500 m and led to a surface cooling of 3 °C due to reduced incoming solar radiation. The results highlight the urgent need to strengthen short-range forecasting of rapid-onset DS by integrating high-frequency observations and improving dust parameterizations, thereby enhancing public health preparedness, aviation safety, and urban resilience.

Mumbai, the Financial Capital of India, experienced a very heavy rainfall event on 26 May 2025, triggered by the earliest advancement of the southwest monsoon in the recent years. The downpour, amounting to nearly 180 mm in 24 h, caused flash flooding and severe urban disruptions. To investigate this very heavy rainfall event, we combine rain gauge records, ERA5 reanalysis, and Automated Weather Observing Systems (AWOS) Ceilometer (CL31) data. The CL31 is deployed by the India Meteorological Department (IMD) under the Ministry of Earth Sciences ‘Mission Mausam’ to monitor accurate cloud base height (CBH) across Indian airports. ERA5 captured the spatial distribution of heavy rainfall consistent with IMD’s gridded rainfall. The reanalysis precipitable water vapour and vertically integrated moisture transport analyses indicated abundant moisture influx from the Arabian Sea. Enhanced moisture convergence along the Mumbai coast was evident prior to the event, supplying favourable conditions for convection. Convective available potential energy (CAPE) showed significant enhancement (3045 J/kg) before the heavy rainfall event over the Mumbai coast. All three CBH observed by CL31 persisted below 1.8 km during the very heavy rainfall episode. The lower CBH over Mumbai indicates the dominance of deep convective clouds, which favour extreme precipitation. The study highlights the synergistic role of early monsoon onset, coastal moisture convergence, and convective cloud development in producing the heavy rainfall event. The present study also demonstrates the first scientific application of AWOS CL31 observations in India for hazard assessment. These results emphasize the growing value of the ‘Mission Mausam’ AWOS network for both aviation meteorology and urban flood preparedness.

Wayanad, a hilly district in northern Kerala, experienced a series of devastating landslides on July 30, 2024, following intense and persistent rainfall. These events resulted in 336 fatalities and damage to over 1,500 houses and structures, marking one of Kerala's deadliest natural disasters. This study utilizes multi-source satellite observations to assess the hydro-meteorological and land surface conditions leading to the event. High-resolution satellite rainfall data reveal a 620 % excess rainfall in July 2024, with over 500 mm of cumulative precipitation recorded between July 29-30, highlighting the extreme nature of the localized event. Analysis of land use and land cover (LULC) changes using high-resolution imagery shows a 72.3 % increase in built-up areas and a 63.9 % rise in deforested land from 2011 to 2024, indicating increasing anthropogenic pressure on the landscape. While the study does not quantify statistical relationships, it qualitatively links these LULC changes with the observed landslides. The findings underscore the growing vulnerability of mountainous regions to landslides under changing land use and extreme weather conditions. The study highlights the potential of integrating satellite rainfall data with land surface monitoring to enhance situational awareness and inform disaster risk reduction strategies. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This study examines the long-term variability (1980-2022) of low-level clouds and their base heights using cloud observations from India Meteorological Department (IMD) over the Indo-Gangetic Plain (IGP) crucial for aviation. For this purpose, synoptic cloud observation data, coded as per world meteorological organisation (WMO) standards, were collected every three hours from four weather stations of IMD namely Amritsar, Delhi, Lucknow, and Patna. Highest prevalence of cloud types namely Stratus (St), Stratocumulus (Sc), Cumulus (Cu), and Cumulonimbus (Cb) was observed during monsoon than pre-monsoon. We have reported the occurrence of Cb clouds during monsoon in the range of 20-50%. Sc clouds show diurnal variation, peaking at 00, 03 UTC, and 15, 18, and 21 UTC. Cu and Cb clouds exhibit maxima in the afternoon during pre-monsoon and monsoon seasons, possibly due to the diurnal cycle of the atmospheric boundary layer height variations. Monsoon cases surpass pre-monsoon at all IGP sites. Notably, Cb with CBH 600-1000m causes maximum rainfall during monsoon, and predominant Cb base heights are 600-1000m, 1000-1500m, and 1500-2000m across decades.

This study investigates the variations in cloud base height (CBH) measured by ceilometer (CL31) at four airports in the Indo-Gangetic Plain (IGP) namely Jaipur (VIJP, 26.83 ˚N, 75.81 ˚E, 383 m), Lucknow (VILK, 26.76 ˚N, 80.89 ˚E, 122 m), Varanasi (VEBN, 25.45 ˚N, 82.86 ˚E, 81 m), and Patna (VEPT, 25.59 ˚N, 85.09 ˚E, 51 m) during the withdrawal phase of the southwest monsoon. The India Meteorological Department (IMD) introduced Vaisala ceilometers at these airports for the first time to enhance aviation meteorological services. Findings indicate that CBH1 decreases as total cloud cover increases across all IGP airports. Among the four locations, VILK exhibits the highest frequency of maximum mean CBH1. Low-level clouds (LLC) are the most prevalent cloud type, occurring more frequently than mid-level clouds (MLC) and high-level clouds (HLC). LLC occurrence follows a distinct diurnal cycle, with maximum CBH1 during the day due to intense solar heating and minimum CBH1 at night due to radiative cooling. Across all airports, CBH1 most commonly falls less than and within the 2500–3000 m range. A significant positive correlation between CBH1 and PM2.5 concentrations at all four IGP sites suggests that aerosol loading systematically invigorates cloud formation. The ceilometer-based CBH1 observations provide critical insights into the spatio-temporal variability of LLC during monsoon transition periods, offering valuable inputs for improving numerical weather prediction models, validation, and aviation safety. The findings also underscore the need for long-term CBH monitoring and high-resolution modeling to quantify aerosol-induced cloud invigoration effects and their implications for regional weather and climate dynamics over the IGP.

The concentrations of atmospheric pollutants are a serious concern due to their adverse impacts on human health. The ventilation coefficient (VC) is an indicator that measures the dispersion capacity of air pollutants (air pollution potential) in the atmosphere, providing insights into air quality. In this study, we aim to investigate the spatio-temporal variation and trends of VC over the Indian subcontinent using India’s first high-resolution regional reanalysis (IMDAA) and global reanalysis datasets (ERA5) for the period 1980-2019. The spatial pattern of the seasonal climatological mean ERA5 and IMDAA derived VC shows a lower magnitude during winter and post-monsoon seasons, indicating poor air quality over the Indian region, especially in the northern parts of India. We noticed a gradual declination of VC during different seasons, implying increasing surface-level air pollutants and worsening air quality over India. The study further investigates the changes of VC during strong phases of El Niño and La Niña events. The results reveal that El Niño significantly impacts air quality over northern and western parts of India during pre-monsoon and monsoon seasons. At the diurnal scale, the VC exhibits the highest magnitude and variability during daytime due to increased dispersion of pollutants and higher human activities, while remaining low and stable during night due to stagnant atmospheric conditions. These essential characteristics of VC are well represented in IMDAA, albeit with some discrepancies. Furthermore, we have examined the fidelity of a machine learning model-Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), in predicting the VC for the year 2019 over Delhi city. Various statistical metrics are computed to evaluate the performance of the CNN-LSTM model. The results confirm that the model successfully predicts the VC compared to observations from ERA5.

This study presents a comprehensive performance evaluation of the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis total aerosol optical depth (AOD) over India. We first use AOD observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2003 to 2020 to evaluate the spatial and temporal patterns of AOD simulated by CAMS. Owing to the lack of aerosol speciation in MODIS, we complement it with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), which provides individual aerosol species such as dust, black carbon, organic carbon, sulfate, and sea salt. The results demonstrate that CAMS exhibit high AOD, similar to MODIS, particularly over the Indo-Gangetic Plain, despite some underestimations in total AOD. Temporal trend analysis indicates that CAMS exhibited rising AOD trends similar to MODIS across Indian regions, though discrepancies arise in western India during pre-monsoon and monsoon seasons. Spatial differences in different aerosol species between CAMS and MERRA-2 suggest potential differences in model parameterizations. Principal component analysis (PCA) further reveals that the first mode of AOD (PCA-1) in both CAMS and MERRA-2 shows a strong correlation (> 0.7) with MODIS during all seasons except pre-monsoon. This enhances our understanding of aerosol distribution and its implications for regional climate and air quality. Consequently, this study provides valuable insights into the performance of CAMS, supporting advancements in climate modeling, air quality management, and environmental policy-making in densely populated and rapidly developing regions.

This study examines the variability of cumulonimbus (Cb) clouds over four weather stations located in the Indo-Gangetic Plain (IGP) and their thermodynamical linkages using the India Meteorological Department synoptic cloud observations (1980–2022) during pre-monsoon and monsoon season. During pre-monsoon and monsoon, the seasonal standardized anomaly of the number of Cb cloud cases showed mixed (both +ve and −ve) anomalies over all IGP stations. During pre-monsoon and monsoon, the number of Cb cloud cases showed a significant declining trend over Amritsar, Lucknow, and Patna. However, Delhi showed an insignificant rising trend in Cb cases during both seasons. The variability in Cb cloud cases during monsoon over IGP stations is consistent with the variability in temperature, which may be attributed to the fact that intense heating/cooling over a moisture-rich IGP region in monsoon triggers/suppresses the formation of Cb clouds. In contrast, the variability in Cb cases during pre-monsoon is consistent with variability in relative humidity. This may be attributed to the presence/absence of moisture over the IGP region in the pre-monsoon season, triggering/suppressing the formation of Cb clouds. Detailed information about Cb cloud anomaly over IGP airports will be very helpful for aviation forecasters in improving weather forecasting and enhancing disaster management capabilities.

We evaluate the performance of Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating aerosol optical depth (AOD) climatology, variability, and trends over the Indo-Gangetic Plain (IGP) considered for the period of space-borne MODIS satellite observations during 2003–2014. The multi-model ensemble (MME) mean of CMIP6 models, is considered when better correlations (≥0.5) of models against the MODIS-derived AOD observations using standard statistical skill metrics. Analysis suggests that the CMIP6 MME successfully reproduces the spatial distribution of AOD climatology and its seasonal variability, albeit with some discrepancies. In particular, CMIP6 MME underestimates AOD spatial distributions across the IGP region (∼ bais varies between 0.2 and 0.4 during different seasons). CMIP6 MME captures coarse-mode aerosol distribution similar to AERONET over the IGP, while it has limited skill in capturing the fine-mode aerosol distribution, which could be due to uncertainties pertinent to anthropogenic aerosol emissions. Furthermore, the CMIP6 MME captures the seasonal evolution of aerosols over the IGP and its sub-regions (i.e., eastern and western IGP regions). Notably, the CMIP6 MME successfully captures the dominance among various aerosol species and their contributions to total AOD over the IGP. The CMIP6 MME mimic the observed AOD trends (varies between 0.01 and 0.03 year−1) well across the study regions, except the western IGP, where the MME is unable to replicate declining AOD trends during the pre-monsoon and monsoon seasons compared to MODIS. By conducting a principal component analysis on AOD data, it is apparent that the CMIP6 MME captures AOD variances that are comparable to MODIS observations, albeit with some discrepancies. This finding of the present research will help in policymaking, and mitigation strategies in the region.

The present study investigates the influencing factors responsible for the asymmetry in aerosol optical depth (AOD) trends using long-term datasets (2003–2019) over western and eastern Indo-Gangetic Plain (IGP) regions during the pre-monsoon season. Analysis from Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2) for different aerosols illustrates that dust aerosols dominate over the western IGP (W-IGP), while sulphate and carbonaceous aerosols (black carbon (BC) and organic carbon (OC)) majorly contributed to the total AOD over the eastern IGP (E-IGP). Our study reveals a significant decline in AOD over the W-IGP, while a rising trend over E-IGP from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. A dipole pattern in AOD trends over IGP indicates the aerosol loading from combined effects of various natural and anthropogenic emissions under favourable meteorological conditions over the west and east IGP, respectively . Furthermore, the declining AOD trend over W-IGP is mainly attributed to increased pre-monsoonal rainfall, which supports the wet deposition, increases soil moisture, thus reducing soil erodibility, and correlates strongly with meteorological factors. The rising AOD trend over the E-IGP appears to be influenced by increased anthropogenic emissions (i.e., BC, OC, and sulphate) from the industrialization of the region, decreased rainfall, and enhanced westerly-induced advection of aerosols from W-IGP. Our study indicates that the regional meteorological variables and anthropogenic sources influence changes in the AOD trends over the IGP region.

This work investigates the spatio-temporal variability of planetary boundary layer height (PBLH) characteristics by leveraging multi-decadal (1980–2019) data from India’s first high-resolution regional atmospheric reanalysis–IMDAA, in conjunction with ERA5 and MERRA-2. The spatial variability in the seasonal and annual climatological mean PBLH obtained from IMDAA agree well with ERA5 and MERRA-2, albeit with some inconsistencies. The IMDAA and ERA5 PBLH exhibits a high correlation (> 0.6) over entire India, and is also exhibits a significant positive (negative) correlation with MERRA-2 over northwest and central (southern and eastern) Indian regions. However, IMDAA tends to overestimate ERA5 PBLH ( ~ < 500 m) and underestimate MERRA-2 PBLH ( ~ > 500 m) during all seasons. Despite these discrepancies, IMDAA successfully captures the diurnal changes in PBLH similar to ERA5 and MERRA-2. Furthermore, the evaluation of IMDAA PBLH with other meteorological factors suggests that PBLH correlates negatively with relative humidity (RH), indicating a decrease in PBLH as RH increases. On the other hand, PBLH shows positive correlations with surface temperature and surface zonal winds. Surface sensible and latent heat flux exhibit positive and negative correlations with PBLH, respectively, over Indian sub-regions throughout all seasons. Moreover, IMDAA realistically represents the declining trend of PBLH (-1.1 to -76.2 m decade− 1) compared to ERA5 in India during all seasons. The results from IMDAA, in concurrence with other reanalyses, demonstrate that the decreasing trend in PBLH over India is associated with rising surface temperatures and weakening surface zonal winds. This trend is attributed to increasing latent heat flux and decreasing sensible heat flux. The changes in surface fluxes over India are attributed to the intensification of Indian monsoon rainfall in the last three decades. Moreover, El Niño appears to be an important control on PBLH variability over India during different seasons, which is realistically represented by IMDAA as in ERA5 and MERRA-2.

Air pollution, particularly ambient particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5), has emerged as a significant global concern due to its adverse impact on public health and the environment. Rapid urbanization, industrialization, and the increased number of automobiles in the cities have led to a significant enhancement in the PM2.5 concentrations to their hazardous level, which indicates the requirement for early warning systems to reduce exposure. Artificial Intelligence and Machine Learning (AI/ML) have come forth as highly sought-after tools widely utilized for air quality (AQ) forecasting. A deep learning based Recurrent Neural Network (RNN) models are highly being used due to their performance in predicting the AQ from the time series data. The present study evaluated three types of RNNs, namely SimpleRNN, Gradient Recurrent Units (GRU) and Long Short-Term Memory (LSTM) to forecast the PM2.5 in the four major Indian metropolitan cities. This research utilizes the daily in-situ PM2.5 data from national AQ monitoring agency in India, known as Central Pollution Control Board (CPCB) for the period 2018 to 2023. Various atmospheric gases and dispersion factors were employed to train model for the prediction of PM2.5 over the cities of Chennai, Delhi, Hyderabad and Kolkata. The ability of the each RNN model is evaluated and compared with observed data using various statistical parameters such as root mean squared error, mean absolute error, and mean absolute percentage error, coefficient of determination and correlation coefficient. Our findings indicate that all three neural networks can capture future PM2.5 trends consistently, albeit with some uncertainty. GRU was the most proficient in estimating PM2.5 levels in all the cities, followed by LSTM and SimpleRNN. The highest accuracy score was observed over Hyderabad followed by Kolkata, Chennai and Delhi.

The frequency and intensity of extreme thermal stress conditions during summer are expected to increase due to climate change. This study examines sixteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that have been bias-adjusted using the quantile delta mapping method. These models provide Universal Thermal Climate Index (UTCI) for summer seasons between 1979 and 2010, which are regridded to a similar spatial grid as ERA5-HEAT (available at 0.25° × 0.25° spatial resolution) using bilinear interpolation. The evaluation compares the summertime climatology and trends of the CMIP6 multi-model ensemble (MME) mean UTCI with ERA5 data, focusing on a regional hotspot in northwest India (NWI). The Pattern Correlation Coefficient (between CMIP6 models and ERA5) values exceeding 0.9 were employed to derive the MME mean of UTCI, which was subsequently used to analyze the climatology and trends of UTCI in the CMIP6 models.The spatial climatological mean of CMIP6 MME UTCI demonstrates significant thermal stress over the NWI region, similar to ERA5. Both ERA5 and CMIP6 MME UTCI show a rising trend in thermal stress conditions over NWI. The temporal variation analysis reveals that NWI experiences higher thermal stress during the summer compared to the rest of India. The number of thermal stress days is also increasing in NWI and major Indian cities according to ERA5 and CMIP6 MME. Future climate projections under different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) indicate an increasing trend in thermal discomfort conditions throughout the twenty-first century. The projected rates of increase are approximately 0.09 °C per decade, 0.26 °C per decade, and 0.56 °C per decade, respectively. Assessing the near (2022–2059) and far (2060–2100) future, all three scenarios suggest a rise in intense heat stress days (UTCI > 38 °C) in NWI. Notably, the CMIP6 models predict that NWI could reach deadly levels of heat stress under the high-emission (SSP5-8.5) scenario. The findings underscore the urgency of addressing climate change and its potential impacts on human well-being and socio-economic sectors.

Abstract The present study investigates the influencing factors responsible for the asymmetry in aerosol optical depth (AOD) trends using long-term datasets (2003-2019)over western and eastern Indo-Gangetic Plain (IGP) regions during the pre-monsoon season. Analysis from MERRA-2 for different aerosols illustrates that dust aerosols dominate over the western IGP (W-IGP), while sulphate and carbonaceous aerosols (black carbon (BC) and organic carbon (OC)) majorly contributed to the total AOD over the eastern IGP (E-IGP). Our study reveals a significant decline in AOD over the W-IGP, while a rising trend over E-IGP from satellite (MODIS) and Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2) data. A dipole pattern in AOD trends over IGP indicates the aerosol loading from combined effects of various natural and anthropogenic emissions under favourable meteorological conditions over the west and east IGP, respectively. Furthermore, the declining AOD trend over W-IGP is mainly attributed to increased pre-monsoonal rainfall, which supports the wet deposition and increases soil moisture, thus reducing soil erodibility, and correlates strongly with meteorological factors. The rising AOD trend over the E-IGP appears to be influenced by increased anthropogenic emissions (i.e., BC, OC, and sulfate) from industrialization of the region, decreased rainfall and enhanced westerly-induced advection of aerosols from W-IGP. Our study indicates that the regional meteorological variables and anthropogenic sources influence changes in the AOD trends over the IGP region.

Filter-based devices were found to underestimate PM2.5 mass concentrations due to the evaporation loss of semi-volatile inorganic materials (SVIM). To reduce the evaporation-induced PM2.5 loss, the chilled Teflon filter sampler (CTF) was developed in which the sampling air was chilled to low temperatures (T) of 4-7 degrees C after dehumidification. The CTF with the aerosol flow dehumidified to low relative humidity (RH) of 25.50 +/- 4.88% by using a Nafion dehumidifier and chilled at 4 degrees C showed an accurate measurement for the total ion concentration with the mean normalized bias (MNB) of +4.17 +/- 8.96% as compared to the actual value measured by the porous denuder sampler (PDS). In comparison, the normal single Teflon filter sampler (STF) sampled PM2.5 ion concentration at ambient T and RH showed a negative MNB of -14.26 +/- 13.66%. It indicates that the 4 degrees C CTF can suppress the evaporation loss of SVIM and measure actual ion concentrations accurately. However, 4 degrees C CTF over-measured PM2.5 concentrations with the MNB of +15.01 +/- 7.99% as compared to -10.40 +/- 5.92% of the STF, due to normal and capillary condensations of water vapor although the condensed water on particles prevented SVIM evaporation loss. After correcting for the remaining water concentration determined by using surrogate TiO2 nanoparticles, the accuracy of PM2.5 concentrations was improved significantly with the MNB of only about +2.93 +/- 8.56%. To avoid excessive remaining water, the CTF was chilled to 7 degrees C and found to be able to reduce the evaporation loss of SVIM while measuring PM2.5 concentrations accurately with the MNB of +4.92 +/- 6.52% without remaining water correction in most sampling days.

Heat waves are quite frequent over the Indian subcontinent during the summer season (April-July) owing to an increase in anthropogenic activities and global temperatures. These extreme heat conditions induce a high level of outdoor discomfort, adverse health effects and mortality, depending on the degree of thermal stress. The present study investigates the climatology of thermal stress and its trends over northwest (NW) India during the summer. The Universal Thermal Climate Index (UTCI) derived from Human thErmAl comforT (ERAS-HEAT) dataset was used for the period of 1981-2019. The monthly and seasonal climatological mean of UTCI exhibits moderate to strong thermal stress over NW India (ranges from 27 to 34.5 degrees C) than in the rest of the country (below 25.5 degrees C), with a peak during the months of June (34.5 degrees C) and July (33.5 degrees C) months. The seasonal mean UTCI shows significant rising trends (0.9 degrees C per 39 years) over NW India and entire India (0.6 degrees C per 39 years), indicating that the thermal discomfort amplifies at a faster pace compared to the rest of India. Similar rising trends are also noticed in the major cities of the study region. Surface temperature and relative humidity also exhibit a substantial increasing trend, which resulted in the intensification of thermal discomfort over NW India. Furthermore, the number of thermal discomfort days over NW India exhibits an increasing trend during 1981-2019. The composite analysis of UTCI greater than 32 degrees C (referred to as strong heat stress) depicts the highest thermal discomfort conditions in NW India. During summer, strong soil temperatures and high sensible heat fluxes over the study region may enhance the warming at the surface during UTCI (> 32 degrees C) days as it depends on surface radiative fluxes through the mean radiant temperature. In addition to high temperatures, a substantial amount of moisture transported by strong westerly wind from the Arabian Sea towards the NW India during strong thermal stress days seems to have contributed to high thermal stress conditions in the region.

BAM-1020 and TEOM-FDMS have undergone rigorous testing and analysis protocols to become the Federal Equivalent Method (FEM) monitors and serve as reliable near real-time monitors for compliance with the National Ambient Air Quality Standards and references for low-cost PM2.5 sensor calibration. However, differences between the FEM and FRM (Federal Reference Method) data still exist, which cause inconsistency in PM2.5 measurements. This study carried out the field tests across five geographically diverse stations in different seasons in Taiwan with 265 daily samples collected by the collocated BAM-1020 and TEOM-FDMS and the FRM sampler and found that the biases between the FEM and FRM values increased with the decreasing PM2.5 concentrations and varied with ambient conditions. The measurement uncertainties exist in the BAM-1020 were mainly due to the aerosol water content, while the TEOM-FDMS always over-measured PM2.5 compared to the FRM sampler since it corrects for the evaporation loss of semi-volatile particle materials. To reduce the biases between the FEM monitors and FRM samplers, empirical equations based on PM2.5 concentrations (mu g m(-3)), temperature (degrees C), and relative humidity (%) were derived to convert the FEM data to the FRM data. After correction, the mean normalized biases were decreased from +1.67 +/- 12.43% to +0.63 +/- 8.75% for the BAM1020 and from +13.86 +/- 14.50% to 0.85 +/- 9.0% for the TEOM-FDMS. Also, the same empirical equation was used to convert the FRM PM2.5 values to the "true" or "actual" PM2.5 values represented by the TEOM-FDMS with the bias reduced from -10.76 +/- 11.42% to +1.33 +/- 8.44% after conversion.

Abstract The louvered 16.7 L min−1 PM10 inlet is commonly used in PM10 and PM2.5 FRM samplers or FEM monitors. Its sampling efficiency is influenced by particle bounce, re-entrainment, and overloading since the PM10 inlet contains a PM10 impactor with an uncoated impaction surface. In this study, a modified PM10 (M-PM10) inlet with an oil-soaked glass fiber filter substrate supported by an oil-soaked porous metal disc was developed to eliminate the particle bounce and overloading effects. The oiled M-PM10 inlet and the traditional PM10 inlets with and without grease coating were collocated at the field for long-term comparison tests. The results show that the traditional uncoated PM10 inlet which is cleaned initially but not cleaned daily afterwards oversamples PM10 concentration after one 24-h sampling day and has the high positive average sampling bias during 14 sampling days due to particle bounce and re-entrainment. The grease-coated PM10 inlet without daily cleaning shows a better performance with a smaller sampling bias, but it still oversamples PM10 concentrations after the first three 24-h sampling days and then undersamples after 10 sampling days due to particle bounce and overloading effects, respectively. In comparison, the M-PM10 inlet shows a good performance with a small average sampling bias during 35 sampling days since vacuum oil wicks up through the deposit to eliminate particle bounce and overloading. It is suggested that the oiled M-PM10 inlet can be used to replace the traditional EPA PM10 inlet and for long-term sampling of over 1 month without the frequent maintenance need. Copyright © 2019 American Association for Aerosol Research

General characteristics of sub-tropical middle atmospheric temperature structure over a high altitude station, Mt. Abu (24.5°N, 72.7°E, altitude ~1670 m, above mean sea level (amsl)) are presented using about 150 nights observational datasets of Rayleigh Lidar. The monthly mean temperature contour plot shows two distinct maxima in the stratopause region (~45–55 km), occurring during February-March and September-October, a seasonal dependence similar to that reported for mid- and high-latitudes respectively. Semi-Annual Oscillation (SAO) are stronger at an altitude ~60 km in the mesospheric temperature in comparison to stratospheric region. A comparison with the satellite (Halogen Occultation Experiment, (HALOE)) data shows qualitative agreement, but quantitatively a significant difference is found between the observation and satellite. The derived temperatures from Lidar observations are warmer ~2–3 K in the stratospheric region and ~5–10 K in the mesospheric region than temperatures observed from the satellite. A comparison with the models, COSPAR International Reference Atmosphere (CIRA)-86 and Mass Spectrometer Incoherent Scatter Extended (MSISE)-90, showed differences of ~3 K in the stratosphere and ~5–10 K in the mesosphere, with deviations somewhat larger for CIRA-86. In most of the months and in all altitude regions model temperatures were lower than the Lidar observed temperature except in the altitude range of 40–50 km. MSISE-90 Model temperature overestimates as compared to Lidar temperature during December-February in the altitude region of 50–60 km. In the altitude region of 55–70 km both models deviate significantly, with differences exceeding 10–12 K, particularly during equinoctial periods. An average heating rate of ~2.5 K/month during equinoxes and cooling rate of ~4 K/month during November-December are found in altitude region of 50–70 km, relatively less heating and cooling rates are found in the altitude range of 30–50 km. The stratospheric temperature derived from the Lidar and columnar ozone observed by the Total Ozone Mapping Spectrometer (TOMS) over Mt. Abu shows good correlation (r 2 = 0.61) and indicates the association of ozone with the temperature.