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Despite the growing number of GNSS sites, GNSS meteorology still suffers from insufficient observational data. This deficiency is particularly evident in GNSS tomography processing in the lowermost part of the troposphere, where the highest variability of weather conditions is observed. To address this challenge, we propose an integrated tomography solution that uses both ground-based GNSS observations and radio occultation (RO) excess phase in the same tomography model. Our integrated approach benefits from improved geometry and increased number of observations. In order to verify presented methodology, we use 41 GNSS stations and 78 RO profiles gathered during four different high precipitation events over the region of southern Poland. The results are validated against radiosonde observations and WRF reanalysis. Our study indicates an enhancement in the representation of wet refractivity on the order of 20 to 30% RMSE decrease when RO are integrated into the tomography solution with the mean error of final integrated product equal 3.5 ppm. [ABSTRACT FROM AUTHOR]

Extensive spatiotemporal analyses of long-trend surface ozone in the Yangtze River Delta (YRD) region and its meteorology-related and emission-related have not been systematically analyzed. In this study, by using 8-year-long (2015–2022) surface ozone observation data, we attempted to reveal the variation of multiple timescale components using the Kolmogorov–Zurbenko filter, and the effects of meteorology and emissions were quantitatively isolated using multiple linear regression with meteorological variables. The results showed that the short-term, seasonal, and long-term components accounted for daily maximum 8-hr average O 3 (O 3–8 hr) concentration, 46.4%, 45.9%, and 1.0%, respectively. The meteorological impacts account for an average of 71.8% of O 3–8 hr , and the YRD's eastern and northern sections are meteorology-sensitive areas. Based on statistical analysis technology with empirical orthogonal function, the contribution of meteorology, local emission, and transport in the long-term component of O 3–8 hr were 0.21%, 0.12%, and 0.6%, respectively. The spatiotemporal analysis indicated that a distinct decreasing spatial pattern could be observed from coastal cities towards the northwest, influenced by the monsoon and synoptic conditions. The central urban agglomeration north and south of the YRD was particularly susceptible to local pollution. Among the cities studied, Shanghai, Anqing, and Xuancheng, located at similar latitudes, were significantly impacted by atmospheric transmission—the contribution of Shanghai, the maximum accounting for 3.6%. [ABSTRACT FROM AUTHOR]

• Maximum daily 8-h (MDA8) O 3 rose by ∼20.7% in June 2015–2019 over North China Plain. • The MDA8 O 3 increase induced by aerosols reduction was limited (∼2.9%). • The MDA8 O 3 increased by ∼9.7% due to the precursor emissions changes. • The meteorological fluctuation led to an increase of ∼6.4% in MDA8 O 3. Continuous aggravated surface O 3 over North China Plain (NCP) has attracted widely public concern. Herein, we evaluated the effects of changes in aerosols, precursor emissions, and meteorology on O 3 in summer (June) of 2015–2019 over NCP via 8 scenarios with WRF-Chem model. The simulated mean MDA8 O 3 in urban areas of 13 major cities in NCP increased by 17.1%∼34.8%, which matched well with the observations (10.8%∼33.1%). Meanwhile, the model could faithfully reproduce the changes in aerosol loads, precursors, and meteorological conditions. A relatively-even O 3 increase (+1.2%∼+3.9% for 24-h O 3 and +1.0%∼+3.8% for MDA8 O 3) was induced by PM 2.5 dropping, which was consistent with the geographic distribution of regional PM 2.5 reduction. Meanwhile, the NO 2 reduction coupled with a near-constant VOCs led to the elevated VOCs/NO x ratios, and then caused O 3 rising in the areas under VOCs-limited regimes. Therein, the pronounced increases occurred in Handan, Xingtai, Shijiazhuang, Tangshan, and Langfang (+10.7%∼+13.6% for 24-h O 3 and +10.2%∼+12.2% for MDA8 O 3); while the increases in other cities were 5.7%∼10.5% for 24-h O 3 and 4.9%∼9.2% for MDA8 O 3. Besides, the meteorological fluctuations brought about the more noticeable O 3 increases in northern parts (+12.5%∼+13.5% for 24-h O 3 and +11.2%∼+12.4% for MDA8 O 3) than those in southern and central parts (+3.2%∼+9.3% for 24-h O 3 and +3.7%∼+8.8% for MDA8 O 3). The sum of the impacts of the three drivers reached 16.7%∼21.9%, which were comparable to the changes of the observed O 3. Therefore, exploring reasonable emissions-reduction strategies is essential for the ozone pollution mitigation over this region. [Display omitted] [ABSTRACT FROM AUTHOR]

• O 3 pollution in Tianjin was largely driven by the changes in precursors emissions. • Meteorology alleviated O 3 pollution in 2015-2016 but aggravated O 3 thereafter. • Temperature is the most important meteorological factor governing O 3 pollution. Deterioration of surface ozone (O 3) pollution in Northern China over the past few years received much attention. For many cities, it is still under debate whether the trend of surface O 3 variation is driven by meteorology or the change in precursors emissions. In this work, a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algorithm were utilized to quantify the meteorological impacts on the recorded O 3 trend and identify the key meteorological factors affecting O 3 pollution in Tianjin, the biggest coastal port city in Northern China. After "removing" the meteorological fluctuations from the observed O 3 time series, we found that variation of O 3 in Tianjin was largely driven by the changes in precursors emissions. The meteorology was unfavorable for O 3 pollution in period of 2015-2016, and turned out to be favorable during 2017-2021. Specifically, meteorology contributed 9.3 µg/m3 O 3 (13%) in 2019, together with the increase in precursors emissions, making 2019 to be the worst year of O 3 pollution since 2015. Since then, the favorable effects of meteorology on O 3 pollution tended to be weaker. Temperature was the most important factor affecting O 3 level, followed by air humidity in O 3 pollution season. In the midday of summer days, O 3 pollution frequently exceeded the standard level (>160 µg/m3) at a combined condition with relative humidity in 40%-50% and temperature > 31°C. Both the temperature and the dryness of the atmosphere need to be subtly considered for summer O 3 forecasting. [Display omitted] [ABSTRACT FROM AUTHOR]

Recently, air pollution especially fine particulate matters (PM 2.5) and ozone (O 3) has become a severe issue in China. In this study, we first characterized the temporal trends of PM 2.5 and O 3 for Beijing, Guangzhou, Shanghai, and Wuhan respectively during 2018-2020. The annual mean PM 2.5 has decreased by 7.82%-33.92%, while O 3 concentration showed insignificant variations by -6.77%-4.65% during 2018-2020. The generalized additive models (GAMs) were implemented to quantify the contribution of individual meteorological factors and their gas precursors on PM 2.5 and O 3. On a short-term perspective, GAMs modeling shows that the daily variability of PM 2.5 concentration is largely related to the variation of precursor gases (R = 0.67-0.90), while meteorological conditions mainly affect the daily variability of O 3 concentration (R = 0.65-0.80) during 2018-2020. The impact of COVID-19 lockdown on PM 2.5 and O 3 concentrations were also quantified by using GAMs. During the 2020 lockdown, PM 2.5 decreased significantly for these megacities, yet the ozone concentration showed an increasing trend compared to 2019. The GAMs analysis indicated that the contribution of precursor gases to PM 2.5 and O 3 changes is 3-8 times higher than that of meteorological factors. In general, GAMs modeling on air quality is helpful to the understanding and control of PM 2.5 and O 3 pollution in China. [Display omitted] [ABSTRACT FROM AUTHOR]

The role of PM 2.5 (particles with aerodynamic diameters ≤ 2.5 µm) deposition in air quality changes over China remains unclear. By using the three-year (2013, 2015, and 2017) simulation results of the WRF/CUACE v1.0 model from a previous work (Zhang et al., 2021), a non-linear relationship between the deposition of PM 2.5 and anthropogenic emissions over central-eastern China in cold seasons as well as in different life stages of haze events was unraveled. PM 2.5 deposition is spatially distributed differently from PM 2.5 concentrations and anthropogenic emissions over China. The North China Plain (NCP) is typically characterized by higher anthropogenic emissions compared to southern China, such as the middle-low reaches of Yangtze River (MLYR), which includes parts of the Yangtze River Delta and the Midwest. However, PM 2.5 deposition in the NCP is significantly lower than that in the MLYR region, suggesting that in addition to meteorology and emissions, lower deposition is another important factor in the increase in haze levels. Regional transport of pollution in central-eastern China acts as a moderator of pollution levels in different regions, for example by bringing pollution from the NCP to the MLYR region in cold seasons. It was found that in typical haze events the deposition flux of PM 2.5 during the removal stages is substantially higher than that in accumulation stages, with most of the PM 2.5 being transported southward and deposited to the MLYR and Sichuan Basin region, corresponding to a latitude range of about 24°N-31°N. [Display omitted] [ABSTRACT FROM AUTHOR]

The idea that a tropospheric heat source generating waves can be replaced with a surface source of pressure oscillations on the Earth's surface is proposed and analyzed. In the surface source, atmospheric pressure fluctuations caused by a tropospheric source are used. The discrepancies due to the replacement of the tropospheric heat source by the surface one are estimated using experiments with a high-resilution numerical model of acoustic-gravity waves. It is shown that at frequencies of infrasound, the wave amplitudes in the upper atmosphere are very close for both tropospheric heat and surface pressure sources. At frequencies of internal gravity waves, the time-spatial structure of the wave fields are close in the upper atmosphere, but wave amplitudes for the surface pressure generation could be larger (up to two times) than those for the respective tropospheric heat sources. This discrepancy is explained and some corrections to the surface pressure source are proposed, which provide better agreement and may be applied for modeling of waves from tropospheric sources, based on the data of atmospheric microbarometer measurements. [ABSTRACT FROM AUTHOR]

China implemented a stringent clean air initiative to improve air quality in 2013. By 2017, fine particulate matter (PM 2.5) levels in the Yangtze River Delta (YRD) region dropped significantly, but ozone (O 3) pollution surged, which aroused concerns about its adverse impact on crops, climate and human health. Due to the complex influences of atmospheric processes, the variation of O 3 is highly nonlinear, making the control of O 3 pollution highly challenging. It is urgent to deepen the understanding of the role of meteorological factors and atmospheric oxidation capacity (AOC) during O 3 episodes. To provide references for O 3 pollution control, the study used the Weather Research and Forecasting model (WRF) and the Community Multi-scale Air Quality model (CMAQ) simulations in the YRD from 2017 to 2020 to analyze the causes of severe O 3 pollution. Observations revealed a slight decline in heavily O 3 polluted days in the YRD region and the simulation results showed that O 3 pollution was more prevalent at temperatures above 25 °C and relative humidity below 80%. The increase in volatile organic compounds (VOCs), hydroxyl radical (OH) and nitrate radical (NO 3) was also more conducive to the formation of O 3 pollution. For areas that are primarily VOC-limited areas, such as Shanghai, a stricter policy on VOC control is needed to reduce O 3 pollution more effectively. • O 3 concentration and its heavily polluted episodes decreased in the YRD from 2017 to 2020. • Heavy O 3 pollution is typically accompanied by high temperatures (>25 °C), low humidity (<80%) and weak winds (-2 m·s−1). • Enhanced atmospheric oxidation (+200%) is a significant cause of O 3 pollution in the YRD region. [ABSTRACT FROM AUTHOR]

• Temperature and specific humidity derived from the latest COSMIC-2 and FY-3D are evaluated by its official products, radiosonde, reanalysis data (NCEP and MERRA-2), and MetOp. • The RMSE is less than 1.5 K and 36 % for COSMIC-2 temperature and specific humidity and the significant latitude differences are revealed by regional analysis. • Considering official products, radiosonde, and MetOp evaluations, the atmospheric parameters from the RO system can be found more consistent. • The Co-localization thresholds and resampling have less than 0.1 K and 5 % impact on temperature and specific humidity evaluation. • The comparison of two occultation systems reveals that COSMIC-2 atmospheric parameters have better performance, enabling better detection of the Earth's atmosphere. The Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), as the follow-on mission to COSMIC, commenced observations from 25 June 2019. COSMIC-2, with its high signal-to-noise ratio (SNR) and excellent stability, is expected to outperform COSMIC in detecting atmospheric parameters while penetrating the lower atmosphere. Therefore, to verify and analyze the detection capability of COSMIC-2 occultation in the neutral atmosphere, we retrieved temperature and specific humidity from COSMIC-2 and FY-3D based on the Able integral and the one-dimensional variational method. The evaluations are carried out on retrieval parameters at different altitudes using official RO products, radiosonde, reanalysis data, and MetOp. A detailed analysis of errors in the evaluation process is provided. The RMSEs for COSMIC-2 temperature and specific humidity are less than 1.5 K and 36 % respectively when radiosonde as validation data. Compared to FY-3D, accuracy improved by 11.1 % and 18.5 % respectively. FY-3D shows better consistency in the evaluation of the RO official products and radiosonde. Temporal and spatial analysis reveals minimal seasonal differences but significant latitude influence on evaluation outcomes. In the evaluation of reanalysis data, COSMIC-2 temperature and NCEP performed well in the troposphere, with a correlation coefficient greater than 0.9. The RMSE of the COSMIC-2 temperature and specific humidity in the MERRA-2 evaluation increased by 50.59 % and 16.65 % over FY-3D. Besides, derived parameters are more consistent with MetOp than radiosonde. Moreover, the co-localization threshold and resampling have less than 0.1 K and 5 % impact on the accuracy of evaluating temperature and specific humidity. [ABSTRACT FROM AUTHOR]

[Display omitted] In this study we examined how outdoor climate affects indoor conditions of a cleanroom used for the preparation of radiopharmaceuticals in the Uppsala university hospital pharmacy, Sweden. Further objectives were to identify associated risk factors to ensure a consistent extemporaneous manufacturing process. Data for two years from the facility monitoring system (with one minute resolution for temperature, relative humidity (%RH), differential pressure) were compared with meteorological outdoor data from Uppsala (Swedish Meteorological and Hydrological Institute, 60-minute mean data for temperature, relative humidity, wind speed and air pressure). The findings of this study indicate a linear relationship between indoor and outdoor temperature for the autumn, winter and spring seasons. The typical summer outdoor diurnal pattern is also seen for indoor temperature. During the study period, the minimum outdoor temperature was −17.5 °C and the maximum 31.4 °C. This wide temperature range also entails a wide range of air humidity from 10 %RH to 100 %RH indoors. Cleanroom temperature and %RH are factors that may affect the quality of medications, especially the risk of microbiological growth in aseptic processes, stability of medications during storage but also may affect handling of for example uncoated tablets or weighing of powder, especially at high %RH for hygroscopic drugs or at low %RH due to static electricity. Further the risk of damage on electrical equipment from electrostatic discharge at low %RH is discussed with a focus on the need for humidity control of cleanrooms and/or systems for mitigation of electrostatic discharge in climates with outdoor temperature in the wintertime below freezing point. [ABSTRACT FROM AUTHOR]

In order to accurately and flexibly capture the correlation structure between two random coefficients in the binomial AR(1) process, we propose a new class of models with copula. We derive some basic properties of the process. Then we discuss the conditional least squares (CLS) and conditional maximum likelihood (CML) estimators, as well as their asymptotic properties. We also investigate the finite-sample performance of the proposed method in simulation studies. Finally, a real data example is provided to illustrate the model. • A flexible binomial AR(1) (FBAR(1)) model based on copulas. • Basic probabilistic and statistical properties. • The CLS and CML estimators and the related asymptotic properties. • Real data applications in the fields of meteorology and finance. [ABSTRACT FROM AUTHOR]

The national lockdown policies have drastically disrupted socioeconomic activities during the COVID-19 pandemic in China, which provides a unique opportunity to investigate the air quality response to such anthropogenic disruptions. And it is meaningful to evaluate the potential health impacts of air quality changes during the lockdown, especially for PM 2.5 with adverse health effects. In this study, by using PM 2.5 observations from 1388 monitoring stations nationwide in China, we examine the PM 2.5 variations between the COVID-19 lockdown (February and March in 2020) and the same period in 2015–2019, and find that the national average of PM 2.5 decreases by 18 μg/m3, and mean PM 2.5 for most sites (about 75%) decrease by 30%–60%. The anthropogenic and meteorological contributions to these PM 2.5 variations are also determined by using a stepwise multiple linear regression (MLR) model combined with the Kolmogorov–Zurbenko filter. Our results show that the change of anthropogenic emissions is a leading contributor to those widespread PM 2.5 reductions, and meteorological conditions have the negative influence on PM 2.5 reductions for some regions, such as Beijing–Tianjin–Hebei (BTH). Additionally, the avoided premature death due to PM 2.5 reduction is estimated as a predicted number based on a log-linear concentration-response function. The total avoided premature death is 9952 in China, with dominant contribution (94%) from anthropogenic emission changes. For BTH, Yangtze River Delta, Pearl River Delta and Hubei regions, the reductions of PM 2.5 are 24.1, 24.3, 13.5 and 29.5 μg/m3, with the avoided premature deaths of 1066, 1963, 454 and 583, respectively. [Display omitted] [ABSTRACT FROM AUTHOR]

In order to advance the expansion of onshore wind energy, wind turbines (WTs) must be erected in often complex terrain. The orography and the associated meteorological conditions, as well as the complex propagation of sound and ground motion waves can lead to increased annoyance of residents. Here a joint investigation of residential noise reports, meteorological, acoustic and ground motion data together with operational parameters of a wind farm in southern Germany is presented, with the objective to assess the annoyance of residents affected by WT emissions. Once strongly annoyed residents had been identified, simultaneous measurements were conducted over a 2-month period while residents used a noise reporting app to document their annoyance. A combination of the data shows that WT-related signals can be detected by acoustic and ground motion measurements in the vicinity of the WTs and, to a lesser extent, at the residential sites. In addition, background noise can be identified well. The app data indicates higher complaint rates in the early morning, evening and night hours. Changes in rotation rate seem to be the cause of annoyance as well as high rotation rates. These findings can be used to adjust WT operation in order to decrease immissions. [ABSTRACT FROM AUTHOR]

Advanced neural network methods can effectively combine temporal and spatial information, allowing us to exploit complex relationships in the prediction of sea surface temperature (SST). Therefore, the authors have developed an attention-based context fusion network model recently, known as ACFN, to enhance the underlying spatiotemporal correlations in SST data. In a previous preliminary long-term evaluation, ACFN has demonstrated substantial improvements over several state-of-the-art baseline models, such as Convolutional Long-Short Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN). However, these methods have not been widely used for predicting SST during typhoons, and their performance has not been thoroughly evaluated. This study focuses on three specific typhoons, In-fa (2021), Lekima (2019), and Rumbia (2018), as case studies. The results show that ACFN consistently outperforms ConvLSTM and PredRNN, as demonstrated during Typhoon In-fa with a mean absolute error of 0.27 °C and a root-mean-square error of 0.33 °C for a 1-day forecast time. This study also examines the impact of forecast time on predictive skill, revealing that the performance of ACFN, as expected, gradually decreases with longer forecast times. This thorough evaluation of the ACFN model provides a valuable reference for using deep learning in predicting regional typhoon SST. In previous work, we developed an attention-based context fusion network model. This spatiotemporal neural network model has been successfully applied to short-term sea surface temperature (SST) prediction in the Bohai Sea, utilizing only current and previous 1- to 10-day SST data as input to forecast SST for the subsequent 1- to 10-day period. In this study, we further evaluated the capability of the model to predict SST during typhoons in the Bohai Sea, and the results were very promising. When predicting SST during Typhoon In-fa, the present spatiotemporal neural network model had a mean absolute error of only 0.27 °C for a 1-day forecast time, and a correlation coefficient of 0.74. On the other hand, comparison with baseline spatiotemporal neural network models shows that the present model is more effective in extracting input information. The current model has resolved the constraint that its predictive capability does not diminish as the forecast time increases, thereby enhancing the rationality of the prediction outcomes. In conclusion, the present spatiotemporal neural network model demonstrates its efficacy in forecasting typhoon-induced SST in the Bohai Sea, offering valuable insights for applications in meteorology and oceanography. • Evaluated SST forecast skill during typhoons in the Bohai Sea using advanced spatiotemporal neural networks. • The dependence of predictive skill on forecast time is further evaluated for each of the three neural networks. • Attention-based Context Fusion Network is proposed for operational use in predicting SST during typhoon events. [ABSTRACT FROM AUTHOR]

The concentrations of volatile organic compounds (VOCs) in the atmosphere are driven by both emissions and meteorology. Additionally, a non-linear relationship has been sown between VOCs and the factors driving their concentrations, warranting further investigation into this relationship. Based on a combination of machine learning and Shapley Additive exPlanation (SHAP) models, the aim of this study is to characterize relationships among VOC concentration, meteorology, atmospheric oxidation, and positive matrix factorization (PMF) source apportionment data for Huaibei in 2022. The concentration of total VOCs (TVOCs) in 2022 was observed to be 6.0–64.9 ppb, with the lowest and highest concentrations being observed in the summer and winter, respectively. The receptor model identified vehicle exhaust (VE) as being the highest contributor to TVOCs, whereas biogenic sources (BS) were marked as being the lowest. Moreover, the effect of the emissions on TVOCs was 42.5%, with solvent sources (SS) and VE being the main sources of VOCs, accounting for approximately 45.8%, whereas meteorological conditions and atmospheric oxidation capacity contributed 50.9% and 6.6%, respectively. Temperature (T) and relative humidity (RH) accounted for more than 60% of the meteorological effects, though this trend varied across seasons. RH in spring and autumn had the greatest impact on VOC concentrations, whereas T was the main factor in summer. Finally, the contribution of VE was the highest among the emission sources in the summer, whereas that of SS was the highest in the winter. Overall, this study showed the feasibility of combining machine learning and SHAP models to elucidate the dynamics of VOCs based on observed data. • Explored the impacts of driving factors on VOC concentrations in Huaibei. • Employed a machine learning method (CatBoost-SHAP) combined with the PMF model. • TVOCs concentrations varied from 6.0 to 64.9 ppb, lowest in summer and highest in winter. • Emissions, mainly from industry (SS) and vehicles (VE), contributed 42.5% to TVOCs. [ABSTRACT FROM AUTHOR]

Scientific studies for countries experiencing climate change have accelerated recently. Turkey is among the countries experiencing climate change. Studies carried out for this country or its basins are of great importance both for the basin and the country. Therefore, Marmara Basin, where Turkey's population density is highest and industrialization is at the most advanced level, is selected in this study. The length of meteorological data is 45 years. The analysis of these data is made with Mann-Kendall (MK) and Spearman Rho (SR), which are classical trend methods, and Innovative Triangle Trend Analysis (ITTA) and Trend Polygon Star Concept (TPSC), which are modern trend methods. When the studies on Marmara basin are examined, there is no study that includes these four methods. Therefore, this study stands out in this respect. Because these analysis methods will reveal a new idea by applying them to hydro-meteorological data within the basin area. Before applying MK method, serial correlation method is applied to the meteorological parameter data of all stations. When the analysis results are examined; a general increasing trend is observed in precipitation data for ITTA and TPSC methods. No trend is observed in precipitation data for MK and SR methods. A general increasing trend is observed in temperature data for ITTA, MK and SR methods. However, a generally decreasing trend is observed in temperature data for TPSC method. While there is generally no trend in relative humidity data for MK and SR methods, an increasing trend is observed for ITTA and TPSC methods. • Classical and Modern Trend Methods were applied to Precipitation, Temperature and Relative Humidity data. • The application of these methods is for Marmara Basin in Turkey. • Two modern trend methods (ITTA and TPSCM) were used for the first time in this study. [ABSTRACT FROM AUTHOR]

The various notions of urbanization, indicators of the urban climate, and air quality are described in this article. Urban air quality management and haphazard planning have an impact on climate change at the regional and global levels. The influence of urbanization, its trends, changes in land use patterns, and indicators of climate change are revised in sections of the literature review. Urban dynamics have an impact on microclimate, hence micro-meteorology and air quality are connected. The impact of changing land use patterns, thermal characteristics of surface materials, urban heat island (UHI) intensity, and emission sources that affect microclimate have all been discussed. Prediction models and their drawbacks, such as the effects of urban climate change, weather, and the dispersion of air pollution. A scenario on urban land use patterns, its effects on the climate, weather, and air quality, as well as a description of the relevant models, are presented in an in-depth review. [Display omitted] • Comprehensive Exploration of Urbanization and Climate. • Urban planning and development with urbanization, microclimate, and air quality. • Unpacking the Urban Heat Island Effect. • Models and their limits for urban climate change. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new class of asymmetric beta-binomial generalized autoregressive conditional heteroscedastic (GARCH) models for bounded integer-valued time series, which can capture the asymmetric impact of positive and negative observations. We study the stationarity conditions of the process and derive the moment and covariance functions. Furthermore, we estimate the unknown parameters using the conditional maximum likelihood (CML) method. The asymptotic properties of the estimators are discussed, as well as their finite-sample performance. Finally, we illustrate the model to real time series data in the field of meteorology. • A new asymmetric beta-binomial GARCH model for bounded Z-valued time series. • Asymmetric impact of positive and negative observations. • CML estimation with asymptotic properties. • A real time series data in the field of meteorology. [ABSTRACT FROM AUTHOR]

The construction and assessment of model trajectories that link multiple mantle states is essential to constrain poorly known mantle convection parameters. Previously, volumetric approaches have been applied to assess the quality of constructed mantle flow trajectories. However, there is a need to assess these trajectories based on their dynamic topography predictions because mantle convection cannot be directly observed and may be inferred via its surface geological expressions. Typical metrics for assessing dynamic topography suffer from the double penalty problem — a prediction that is correct in intensity, size, and timing, but incorrect in location, results in large root-mean-square errors when compared to an observation. Here, we introduce metrics, gleaned from meteorology, that decompose any number of dynamic topography fields into their distinct objects after which the similarity between objects is compared. We find that this object-based approach overcomes double penalty and assesses models in a robust manner by providing the ability to assess separately the quality of match between subsidence and uplift areas. Additionally, the approach allows independent quality assessment of multiple aspects of a dynamic topography field, including amplitude and location of dynamic topography. [Display omitted] • Linking mantle flow to dynamic topography is key to constrain mantle properties. • Currently used methods for assessing dynamic topography are limited by double penalty. • Meteorological assessment approaches can overcome this limitation. [ABSTRACT FROM AUTHOR]

• Two novel metrics were used to as indicators for intra-day TV and inter-day TV. • Two indicators are associated with increased mortality risk. • The association varied by climate areas, seasons, and Köppen–Geiger climate types. Temperature variability (TV) is associated with increased mortality risk. However, it is still unknown whether intra-day or inter-day TV has different effects. We aimed to assess the association of intra-day TV and inter-day TV with all-cause, cardiovascular, and respiratory mortality. We collected data on total, cardiovascular, and respiratory mortality and meteorology from 758 locations in 47 countries or regions from 1972 to 2020. We defined inter-day TV as the standard deviation (SD) of daily mean temperatures across the lag interval, and intra-day TV as the average SD of minimum and maximum temperatures on each day. In the first stage, inter-day and intra-day TVs were modelled simultaneously in the quasi-Poisson time-series model for each location. In the second stage, a multi-level analysis was used to pool the location-specific estimates. Overall, the mortality risk due to each interquartile range [IQR] increase was higher for intra-day TV than for inter-day TV. The risk increased by 0.59% (95% confidence interval [CI]: 0.53, 0.65) for all-cause mortality, 0.64% (95% CI: 0.56, 0.73) for cardiovascular mortality, and 0.65% (95% CI: 0.49, 0.80) for respiratory mortality per IQR increase in intra-day TV 0–7 (0.9 °C). An IQR increase in inter-day TV 0–7 (1.6 °C) was associated with 0.22% (95% CI: 0.18, 0.26) increase in all-cause mortality, 0.44% (95% CI: 0.37, 0.50) increase in cardiovascular mortality, and 0.31% (95% CI: 0.21, 0.41) increase in respiratory mortality. The proportion of all-cause deaths attributable to intra-day TV 0–7 and inter-day TV 0–7 was 1.45% and 0.35%, respectively. The mortality risks varied by lag interval, climate area, season, and climate type. Our results indicated that intra-day TV may explain the main part of the mortality risk related to TV and suggested that comprehensive evaluations should be proposed in more countries to help protect human health. [ABSTRACT FROM AUTHOR]

This study compares the convective parameters between two 8-year periods of hail reporting in Poland: 1948–1955 (period A) and 2015–2022 (period B). The data on hail incidents for period A was derived from hail annuals of the Institute of Meteorology and Water Management, while for period B from the European Severe Weather Database. Period A contains more hail reports (18375) compared to the recent period B (3677). ERA5 reanalysis and thundeR package were used to calculate convective parameters for both periods. Convective environments indicated a more favourable environment for the hail occurrence in period B with larger instability, vertical wind shear and storm-relative helicity. The most widespread event with the largest hailstone ever documented in Poland (14 cm) also occurred in period B, on 24 June 2021. However, the mean number of hail reports per day was higher in period A due to a more effective method of hail reporting. In both periods convective environments were also clearly more favourable for hail in the days with hail exceeding 5 cm in diameter compared to the days with smaller hail and in days with the large quantities of hail reports. This study showed that ERA5 was able to capture convective environments reliably well for events as old as the 1950s. • Comparison of the ERA5 environments for hail between 1948 and 1955 and 2015–2022. • Hail environment as more favourable for period 2015–2022. • Better environment for larger hail was found for both periods. [ABSTRACT FROM AUTHOR]

Wheat streak mosaic (WSM) has long been considered the most serious disease of winter wheat in the Great Plains of America. The most effective control method of the WSM disease complex is to manage volunteer wheat that is often caused by the pre-harvest hailstorms in the Great Plains. A significant WSM disease outbreak over a large geographic area in western Nebraska, United States, in the 2017 wheat growing season was studied in relation to a pre-harvest hail in 2016 using open-data Landsat and Sentinel imagery and NOAA MRMS MESH hail data. Wheat growing areas were delineated and a WSM disease severity heat map was generated based on field NDRE readings from the satellite images. The spatial relationship between the hail event and WSM disease severity was demonstrated. For this extensive hail event, the impact from the WSM disease complex extended well beyond the area of impact from the pre-harvest hail. The dramatic extent of the virus impacts beyond the area of direct hail impact demonstrated in this study, strongly reinforces the need for community-wide attention to virus management in cases where large hail streaks occur at the optimum time for the development of pre-harvest hail (ca. within the 3 weeks before harvest). These findings not only agree with the assumption that the pre-harvest hail damaged wheat fields serve as the source fields of mites and virus, but also improves our understanding of the spatial pattern of virus spread which can benefit growers in making management decisions. The remote sensing methods used to delineate these findings proved valuable tools in studying this relationship. • NOAA hail data used to delineate spatial hail pattern and establish WSM risk. • NDRE vegetation index derived from satellite imagery can be used to delineate WSM severity. • Spatial relationship between the hail one year and WSM disease the next documented. • Virus risk extends well beyond the area of direct hail impact. • Community-wide management of volunteer wheat critical after large hail events 3 weeks prior to harvest. [ABSTRACT FROM AUTHOR]

Understanding the full propagation process of drought, from meteorological drought (MD) to vegetation drought (VD), could promote drought monitoring and early warning in the ecosystems. This study characterized MD, soil moisture drought (SD), and VD and their propagations using the Standardized Precipitation Evapotranspiration Index (SPEI), the Soil Moisture Anomaly Percentage Index (SMAPI), and the Vegetation Health Index (VHI) to enhance understanding of the drought hazard in the Yellow River Basin (YRB), China. Drought events, duration, magnitude, intensity, and interval were identified from these time series via the run theory and percentile thresholds. The relationships between MD, SD, and VD were examined by cross-correlating the SPEI, SMAPI, and VHI. Finally, the effects of climate and grid properties on drought characteristics and propagations were investigated. The results showed that the upland had a low risk for all droughts, while the midland had a low risk for MD and a high risk for SD, and the lowland had a high risk for MD in the YRB. For the full-time data, the lowland had the strongest correlations and shortest time lags, while the upland had the weakest correlations and longest time lags for full propagation from MD to SD to VD. However, seasonality and multi-threshold characteristics in extreme drought dominated the propagation process of VD. For monthly data, midland showed the strongest correlations and shortest time lags in summer, while lowland showed the strongest correlations and shortest time lags in other seasons for full propagation of VD. For multi-threshold data, the 0–30th percentile had stronger correlations and shorter time lags for full propagation of VD. Meteorology showed a stronger correlation with all drought characteristics and drought propagation than soil properties and land use. These findings provide valuable insights for enhancing the vegetation aspects of drought monitoring and early warning in the YRB. [ABSTRACT FROM AUTHOR]

The frequent occurrence of air pollution in winter has aroused widespread concern. Identifying the detailed source composition of PM 2.5 and assessing the reduction effects of different emission sources are crucial steps in improving pollution levels. In this study, we conducted a comprehensive evaluation of emission source reductions, encompassing ten local and ten regional sources. Furthermore, we analyzed the influence of meteorological changes on the abatement of PM 2.5 in Tangshan, Shijiazhuang, Xingtai, and Handan during the winter period from 2016 to 2018. Our findings indicate that, in comparison to the winter of 2016, there were significant decreases in the average concentrations of PM 2.5 during the winter of 2017. These decreases were attributed to favorable meteorological conditions and emission reductions. However, during the winter of 2018, there was a rebound in PM 2.5 concentrations for all four cities. This rebound was primarily caused by adverse meteorological diffusion conditions. Throughout the entire research period spanning from the winter of 2016 to 2018, emission reduction played a significant role in PM 2.5 abatement, contributing to 68% in Tangshan, 87% in Shijiazhuang, 93% in Xingtai, and 96% in Handan, respectively. The source apportionment analysis revealed that industrial and residential sources were the dominant contributors to PM 2.5 concentrations in the cities. These findings not only confirm the effectiveness of existing industrial and residential pollution control policies but also highlight the imperative need for implementing non-point source control and joint control measures for regional elevated sources in the future. • Drivers of 2016–2018 trends in winter PM 2.5 reduction was identified. • Emission cut played a significant role in PM 2.5 abatement during 2016–2018 winter. • Industrial and residential emission cut dominated the PM 2.5 decreases. [ABSTRACT FROM AUTHOR]

Solar energy plays an essential role in achieving carbon goals and mitigating climate change. Therefore, solar power plants are rapidly developing in the renewable energy sector. However, many reports of solar power plants are on land, and extremely limited observational research has been conducted on the impacts of fishery complementary photovoltaic power plants (FPVs) on near-surface meteorology and surface energy. This study selected two adjacent eddy covariance observational towers at the fishery complementary photovoltaic power plant (FPV) in Yangzhong, Jiangsu Province, China, to explore this topic. The results indicated that the percent frequency of east wind (<4 m s−1) at 2 m decreased by 25.3% in the FPV site compared with the reference site. The FPV array has not an obvious heating effect on the ambient environment. The average air temperature difference in the two sites at 2 m and 10 m was 0.3 K and 0.1 K, respectively, during the heating period. The net radiation increased by 47.8 W m−2 in the two sites. The sensible heat increased by 7.9 W m−2 due to the heating effect. The latent heat in the two sites is contrary to the sensible heat (−13.0 W m−2). The difference in the water storage heat is 32.18 W m−2, which implies that the water absorbs higher heat in the FPV site than in the reference site. This work illustrated the importance of observational experiments to animate process-based understanding combined with FPV systems and provides a scientific basis for establishing FPV energy balance models based on observations that may be used to reveal the impact of utility-scale FPV deployment on climatic effects. [ABSTRACT FROM AUTHOR]

• In the paper the ionospheric manifestations of tropical cyclones are considered. • We studied cyclones 4–5 category in quite geomagnetic conditions. • Ionosphere electron density data obtained by ESA Swarm mission. • The identified disturbances in the ionosphere have acoustic-gravity parameters. • The statistics of the detected phenomena with relative amplitude 4–10% is given. The paper discusses the issues of the disturbances allocations in the ionosphere that are associated with large atmospheric events - tropical cyclones of the 4–5 categories of 2014. On the basis of the characteristic spatio-temporal characteristics of the acoustic-gravity waves from atmospheric disturbances, the method is proposed for the detection of relative disturbances of the electron concentration in the frequency band characteristic of the disturbances. The observation of electron density by Langmuir probes carried out on the ESA Swarm satellites were used as experimental data. In the paper we consider all large 2014 Tropical Cyclones (TC) and discuss the statistics of the identified disturbances from TC in the ionosphere against the background of regular changes in the electron density in quiet geo-heliomagnetic conditions. The obtained results show that the wave mechanism of disturbance transmission from an atmospheric source to ionospheric heights is not the only one. [ABSTRACT FROM AUTHOR]

The purpose of the data assimilation is to use all the available information to determine as accurately as possible the state of the atmosphere. In this context we consider the assimilation of the GPS-ZTD (Global Positioning System – Zenith Total Delay) by a nudging scheme in Regional Atmospheric Modeling System at Institute of Atmospheric Sciences and Climate (RAMS@ISAC) at medium horizontal resolution (5 km). The water vapour mixing ratio ( q v ) given by the RAMS@ISAC is perturbed according to the differences between observed and simulated ZTD. To verify the impact of the GPS-ZTD data assimilation on the representation of the atmospheric humidity field, especially at the local scale, a numerical experiment was performed over central Italy area, over a one-month time frame. The studied network consists of 41 geodetic receivers and 3 single frequency receivers, this latter category of receivers has turned up more and more in marketing for the very low cost with respect to high level of performance. For what concerns all devices, processing was carried out in PPP (Precise Point Positioning) using goGPS, an Open Source Program Package for GNSS Positioning. For the application of PPP processing to single-frequency receivers, goSEID method (an alternative implementation of the SEID approach integrated in the goGPS open source software) was applied; the algorithm, starting from the observations of a unique (or more) dual frequency "reference" receiver, is able to generate L2 synthetic observations for any single frequency receiver located in its surroundings (15–20 km). Results show that the assimilation of GPS-ZTD has a positive impact on the simulation of the Integrated Water Vapour over the areas where GPS receivers are located. The results obtained, using data assimilation, were indeed compared with those obtained by the model without data assimilation of GPS-ZTD. The two outputs were subsequently evaluated against observations obtained from two geodetic receivers (ROUN and INGR) not used in the assimilation process. The result showed that the independent observation was more consistent with the model output in which also the single-frequency receivers had been assimilated. The assimilation of GPS-ZTD improves also the simulated precipitation, especially by reducing false alarms. [ABSTRACT FROM AUTHOR]

• More than double as many overvoltage events on Sundays compared to weekdays. • No apparent effect of highly variable irradiance on overvoltage incidence. • Duration of overvoltage events matches high irradiance events and last a few minutes. • Frequently reporting AMI do not indicate weak grid segments. If rooftop-installed solar energy systems generate more energy than locally consumed, the excess is fed into the electricity grid, increasing the voltage. Rising penetration levels of solar photovoltaic (PV) systems increase voltage levels, thereby threatening power quality. The extent to which solar PV cause grid issues in actual, nation-wide distribution grids, and how these issues correlate with cloud conditions and irradiance variability has yet to be quantified. This work provides a spatial and temporal characterization of overvoltage events linked to solar PV, using novel data sources. The analysis is based on over 200,000 events from advanced metering infrastructure (AMI) spanning 1/3rd of the Netherlands, combined with satellite observations and 1-minute irradiance measurements. As a result, we find that the typical duration of overvoltage events is in the order of 5 min, and frequently-reporting meters are geographically dispersed. While overvoltages are driven by high PV generation, we do not find evidence that local, short-term irradiance peaks result in additional events as compared to clear sky conditions. However, we do find that median overvoltage event occurrence on Sundays is more than 2.1 times that of weekdays, which can be related to low energy consumption. Our findings indicate PV hosting capacity to be reached throughout the service area simultaneously, and surprisingly show no reduction in event duration by inverter- or grid control. Notably, while a sharp increase in occurrence is observed, overvoltage events are still scarce in absolute terms, with only 0.1% of the AMI reporting more than 10 events in spring-summer 2020. [ABSTRACT FROM AUTHOR]

The drought indices are common tools for monitoring and risk assessment of the drought. Based on copula function, this paper proposes a bivariate combined drought index (BCDI bcf) that can comprehensively characterize the meteorological characteristics (precipitation) and hydrological characteristics (runoff). Besides, the cross-wavelet transform (XWT) is introduced to research the relationship of drought, and identifies the lag in the propagation process. Take Huayuankou station as an example, results indicated: (1) The BCDI bcf combines the merit of the standardized precipitation index (SPI) in capturing the onset of drought and that of the standardized runoff index (SRI) in capturing the end of drought, which contains information on both SPI and SRI, can characterize two types droughts of hydrology and meteorology simultaneously. (2) The characteristics of hydrology and meteorology have significantly periodic, the most obvious on the scale of 2-4a. (3) There is the significant positive statistical correlation between precipitation and runoff, and hydrological droughts are lagging behind meteorological droughts by 1–2 months at Huayuankou station. The BCDI bcf can identify drought more comprehensively and provide strong support for drought detection and prevention. [ABSTRACT FROM AUTHOR]

Alexandria Eastern Harbor (AEH) is constantly enduring the destructive problem of storm surges. Therefore, the investigation of the behavior of surges in the Harbor is crucial, and determining to which extent this is affected by the meteorological conditions is essential. We used hourly data of sea-level, from 10.06.2018 to 15.06.2020, to calculate AEH surge heights. Simultaneous records of winds and sea-level pressure (SLP) were used to examine their impact on the calculated surges. Also, an empirical equation was derived to estimate the surge height in AEH by knowing the SLP and winds. Results revealed a 99% contribution of surges to the recorded sea-level. Surge heights exhibited a seasonal pattern with lower values in spring and higher ones in summer. While the positive surges dominated the Harbor all the year-round, the negative surges appeared only in 8 months: January-June, in addition to October and November. The total and the positive surges were directly proportional to all wind components but the negative parallel ones. Contrariwise, the negative surge associated the decrease of all wind components except the negative parallel and the negative normal components. There was an inverse relationship between the total, positive and negative surge heights and the SLP. [ABSTRACT FROM AUTHOR]

Analyzing the influencing factors of fine particulate matter and ozone formation and identifying the coupling relationship between the two are the basis for implementing the synergistic pollutants control. However, the current research on the synergistic relationship between the two still needs to be further explored. Using the Geodetector model, we analyzed the effects of meteorology and emissions on fine particulate matter and ozone concentrations over the "2 + 26" cities at multiple timescales, and also explored the coupling relationship between the two pollutants. Fine particulate matter concentrations showed overall decreasing trends on inter-season and inter-annual scale from 2015 to 2021, whereas ozone concentrations showed overall increasing trends. While ozone concentrations displayed an inverted U-shaped distribution from month to month, fine particulate matter concentrations displayed a U-shaped fluctuation. On inter-annual scale, climatic factors, with planet boundary layer height as the main determinant, have higher effects for both pollutants than human precursors. In summer and autumn, sunshine duration had the most influence on fine particulate matter, while planet boundary layer height was the greatest factor in winter. Fine particulate matter is the leading impacting factor on ozone concentrations in summer, and there were positive associations between them on both annual and seasonal scale. The impact of nitrogen oxides and volatile organic compounds for both pollutants concentrations varied significantly between seasons. The two pollutants concentration were enhanced by the interactions between the various components. On inter-annual scale, interactions between the planet boundary layer height and other factors dominated the concentrations of the two pollutants, whereas in summer, interactions between fine particulate matter and other factors dominated the concentrations of ozone. The study has implications for the treatment of atmospheric pollution in China and other nations and can serve as an important reference for the creation of integrated atmospheric pollution regulation policies over the "2 + 26" cities. [Display omitted] • Meteorology have stronger effects than emissions on inter-annual scale. • PM 2.5 plays the dominant role on O 3 formation in summer. • There is positive correlations between PM 2.5 and O 3 on inter-annual and inter-season scale. • Interactions between the factors exerted enhanced effects on the PM 2.5 and O 3 concentrations. [ABSTRACT FROM AUTHOR]

Degraded mulch pollution is of a great concern for agricultural soils. Although numerous studies have examined this issue from an environmental perspective, there is a lack of research focusing on crop-specific factors such as crop type. This study aimed to explore the correlation between meteorological and crop factors and mulch contamination. The first step was to estimate the amounts of mulch-derived microplastics (MPs) and phthalic acid esters (PAEs) during the rapid expansion period (1993–2012) of mulch usage in China. Subsequently, the Elastic Net (EN) and Random Forest (RF) models were employed to process a dataset that included meteorological, crop, and estimation data. At the national level, the RF model suggested that coldness in fall was crucial for MPs generation, while vegetables acted as a key factor for PAEs release. On a regional scale, the EN results showed that crops like vegetables, cotton, and peanuts remained significantly involved in PAEs contamination. As for MPs generation, coldness prevailed over all regions. Aridity became more critical for southern regions compared to northern regions due to solar radiation. Lastly, each region possessed specific crop types that could potentially influence its MPs contamination levels and provide guidance for developing sustainable ways to manage mulch contamination. [Display omitted] • Northern and western China had more severe mulch contamination than other regions. • Low temperatures play a crucial role in the nationwide MPs contamination. • In northern and southern China, aridity and radiation time are important factors. • Crop factors are more significant than meteorological factors for PAEs contamination. • The unique combination of crops and weather affects different parts of China. [ABSTRACT FROM AUTHOR]

High concentrations of surface ozone in urban and industrial regions worldwide have been a major air quality issue. Tehran, as the capital and the most populous city of Iran, has experienced intense ambient air pollution over the last three decades. This study aims to assess the long-term trends of ozone over this city in recent decades. Data series of maximum 8-h daily average (MDA8) ozone were obtained for the period between 2007 and 2021. To determine the MDA8 ozone trend attributed to the long-term changes in ozone precursor emissions, we isolated the effects of meteorology and short-term ozone variability. The results show a downward trend from 2007 to 2014 (P1) and an upward trend from 2014 onward (P2). The long-term precursor emissions changes contribute to a 29.1% negative trend (=−0.7 ppb year−1) over P1 and an 8.95% upward trend (=0.1 ppb year−1) over P2. To find the causes of the trend transition, we investigated the relationship between the adjusted MDA8 and several ozone precursors, namely NO x and CO. It is found that, over P1, the dominant chemistry regime is mixed, in which the decline of CO and NO x (attributed to industrial sources) is associated with the downward trend of ozone. On the other hand, the upward trend of ozone over P2 is associated with an increase in NO x mostly attributed to road transport (passenger cars) and residential sectors. • Surface ozone trend over the megacity of Tehran in the last decade is derived. • The influences of meteorology and short-term variability on the trend are minimized. • A transition point in the ozone trend attributable to road transport is found. [ABSTRACT FROM AUTHOR]

The boundary layer plays a vital role in governing the local atmospheric dynamics and meteorology, including the vertical transport of moisture, momentum, energy, and air pollutants. Hence, accurate parameterization of the boundary layer processes is essential in atmospheric models such as the regional climate models (RCMs). However, evaluations of different boundary layer schemes within the Weather Research and Forecasting (WRF) model remain nearly non-existing over complex terrains of the Himalayan region. In this regard, six different planetary boundary layer (PBL) schemes: Yonsei University (YSU), Mellor-Yamada-Nakanishi-Niino level 3 (MYNN3), Shin-Hong Scale-aware (SHSS), Mellor-Yamada-Janjić (MYJ), Asymmetric Convective Model version 2 (ACM2), and Quasi Normal Scale Elimination (QNSE) in the WRF model have been evaluated against observations during the Ganges Valley Aerosol Experiment (GVAX). The evaluation is carried out for clear-sky conditions during spring (March 22–27, 2012) against surface-based, balloon-borne, and radar wind profiler (RWP) observations. The MYJ, YSU, and SHSS performed well in simulating the temperature and specific humidity, whereas the ACM2 (MYNN3) shows lower (higher) bias in T2 and higher (lower) bias in Q2. Model performance is limited in reproducing wind field (r ∼ 0.3) with different PBL schemes over this region. Model performance is seen to vary significantly with the choice of PBL schemes; nevertheless, all schemes could typically capture the daytime maxima in boundary layer height with some overestimation. The competing effects of synoptic versus local circulation were found to control the boundary layer evolution over the Himalayas, affecting the model performance. The model better reproduced the boundary layer evolution during strong surface flow regimes, while a prominent nocturnal peak in boundary layer height is observed during the weak mean flow conditions associated with topography-induced local circulations. The model performance is limited for reproducing such mountain meteorological features in contrast to the homogeneous terrains where such nocturnal peaks are absent. This study may serve as a reference for selecting a suitable PBL scheme for regional climate modeling studies in the future. • Impacts of PBL schemes on meteorology investigated over the Himalayan region. • MYJ, YSU, and SHSS better simulate the PBL variables and structure. • The nocturnal boundary layer peak is challenging to simulate by the model. • Interaction of flow field and orography strongly control the boundary layer evolution. [ABSTRACT FROM AUTHOR]

Biomass burning (BB) greatly impacts the Maritime Continent through various mechanisms including agricultural burning, land clearing and natural response to drought. The dynamic characteristics of BB in terms of its spatiotemporal distribution, seasonality, transport mechanism, and aerosol properties have prompted numerous research efforts including field campaigns, in-situ measurements, remote sensing, and modelling. Although the differing perspectives of these studies have offered insights on understanding the regional BB issues, it is challenging to compare and resolve the wider picture because of the diversity of approaches. Human-induced global warming has certainly caused multiple observed changes in the regional meteorological characteristics. In this study, we review BB events in the Maritime Continent from 2012 to 2021, focusing on the meteorological influence and knowledge evolution in cloud-aerosol-radiation (CAR). Unlike other reviews, our review examines the occurrence of BB events using synergistic application of ground-based measurement, global reanalysis model and satellite product, which allows us to examine the anomalies for comparison with other studies and identify the unique features of the event. We identified four dominant modes of variability responsible for the occurrence of large-scale BB in the Maritime Continent: (1) El Niño Southern Oscillations (ENSO), (2) extreme positive-Indian Ocean Dipole (pIOD), (3) tropical cyclone (TC) activity, and (4) Madden-Julian Oscillations (MJO). We reconcile the past CAR studies and summarize their findings based on the four key CAR mechanisms: (1) instantanous radiative forcing from aerosol-radiation interactions, IRFari (2) and its subsequent adjustments, SAari, (3) instantanous radiative forcing from aerosol-cloud interactions, IRFaci, and (4) and its subsequent adjustments, SAaci. We urge future CAR studies in the Maritime Continent should focus on accurate characterization of the composition of biomass burning plume which is a mixture of peatland, agricultural burning and anthropogenic sources. • Biomass burnings (BBs) in Maritime Continent (MC) from 2012 to 2021 are reviewed and revisited. • BBs are revisited by synergistic usage of ground-reanalysis-satellite product. • Four dominant roles are found to be responsible for the occurrence of BB in MC. • Cloud-aerosol-radiation (CAR) interactions are a lot more complicated than the Twomey effect. • Intensive campaigns focused on aerosol composition in Maritime Continent are necessary. [ABSTRACT FROM AUTHOR]

The high-resolution spatio-temporal monitoring through low-cost sensors (LCS) provides important insights into the dynamics of localized pollution patterns and in conceptualizing effective environmental management and public health interventions. We assessed the role of meteorology and local sources on spatially distributed PM 2.5 pollutants measured from a unique urban-rural scaled network of LCS in Lucknow, India. The city-wide average PM 2.5 annual cycle ranged between 29 ± 7 μg/m3 during the monsoon to 64 ± 30 μg/m3 during the rest of the year. During non-monsoon seasons, >60% of air mass trajectories indicated regional level transportations primarily from the north-western plains of the Indo-Gangetic basin, with peak weighted concentration weighted trajectory (WCWT, scale: 0–100) value estimated at >70. To analyze the impact of local sources, we designed a statistical classification method to divide each seasonal distribution into five incremental concentration groups ranging from no risk zone to hotspots. The hotspots were identified both within urban and rural regions with their average concentration measured as 23 μg/m3 and 26 μg/m3 higher than the rest of the regions in pre-monsoon (2 hotspots) and post-monsoon (4 hotspots) season, respectively. Further analysis shows that the nighttime concentrations were much higher in several locations (up to 40% in pre-monsoon and 54% in post-monsoon compared to their daytime levels), indicating the greater impact of local sources in the presence of low boundary layer height. A diurnal trend analysis along with conditional bivariate probability function (CBPF) was performed to interpret the characteristics and locations of the dominant sources. The study highlights the importance of a dense network of LCS to scale air quality monitoring in spatially heterogeneous environments and as a futuristic tool for PM 2.5 exposure-based studies. [Display omitted] • Statistical classification method developed to analyze AQ data into pollution zones. • Long range transport is prominent from north-western areas in non-monsoon seasons. • Hotspot formation by localized sources can be identified during different seasons. • Nighttime traits with low PBLH offered ideal conditions for hyperlocal variations. • Rural hotspots exhibited distinct pollution dynamics and sources than urban areas. [ABSTRACT FROM AUTHOR]

This study aimed to assess the environmental risk and human health risks associated with PM 2. 5 -bound metals in Paradip city between January 2019 and December 2021. The seasonal average concentrations of PM 2.5 were measured 91.43 ± 70.18 μg m−3, 103.40 ± 60.80 μg m−3, 124.74 ± 62.37 μg m−3, and 159.37 ± 77.88 μg m−3 in pre-monsoon, monsoon, post-monsoon, and winter season respectively. The highest and lowest concentrations are estimated in the winter and pre-monsoon season. Paradip city experienced tropical weather conditions with a hot and humid climate. The wind pattern shows that the predominant wind direction was observed from the south-south-west (SSW) direction. The metals in PM 2.5 were analysed using an atomic absorption spectrophotometer (AAS) by air-acetylene flame using a hollow cathode lamp. The average metal concentration decreased in the order of Fe > Al > Zn > Pb > Cr > Mn > Ni > Cu > Co > Cd > As. The value of the geo-accumulation index (Igeo) was evaluated >1 for Cd, Fe, and Zn elements. The health risk assessment (HRA) results showed that non-carcinogenic risk (NCR) was higher through the inhalation route followed by ingestion and dermal contact. The cumulative NCR, which is expressed in terms of the hazard index (HI), is greater than 1 for infant (2.78E+00), child (2.53E+00), and adult (1.04E+00) via inhalation pathway. The total carcinogenic risk (TCR) for infants, children, and adults was estimated at 1.45E-04, 7.24E-05 and 1.25E-05, respectively, which exceeded the acceptable limit of 1.00E-06. Our comprehensive research plays an important role in both policymakers and relevant stakeholders for the preparation of city action plans concerning ambient air pollution, which can improve the air quality in and around Paradip city, India. [Display omitted] • The highest PM 2.5 and heavy metals concentrations were measured in the winter season. • The Contamination level of selected heavy metals was assessed by the Geo-accumulation Index (I geo). • Health risks associated with metals are assessed as non-carcinogenic and carcinogenic for infants, children, and adults. • The hazard index (HI) is greater than 1 for all age groups via the inhalation pathway. • Total cancer risks (TCR) were found above the acceptable limit of 1.00E-06. [ABSTRACT FROM AUTHOR]

Atmospheric microplastics (AMPs) transmitted through the atmosphere are critical to global microplastic pollution. However, little is known about the long-term dynamics and distribution patterns of deposited (DAMPs) and suspended (SAMPs) AMPs. In this study, simultaneous sampling was conducted over one year to examine the deposition-suspension distribution of AMPs. Monthly and seasonal variations in abundance were evident, with an annual average of 302.31 ± 107.40 items/m2/day for DAMPs and 1.31 ± 0.62 items/m3 for SAMPs. The dynamics of DAMP and SAMP abundance demonstrated the dynamic distribution of AMPs between deposition and suspension. Both meteorological factors and particle features were found to influence the AMP distribution, manifesting as morphological differences and abundance variations. AMPs were most likely derived from traffic, industry, construction, and synthetic textiles, with diverse source areas up to 1750 km away. The estimated deposition flux of 7.28 × 1014 items per year and inhalation exposure of up to 12,777 items per year highlight the potential ecological and health risks of AMPs. [Display omitted] • Microplastic abundance showed a strong temporal variability during one year. • Distinct morphologies were found between deposited and suspended microplastics. • Meteorology influenced the deposition-suspension of atmospheric microplastics. • Primary sources included traffic, industry, construction, and textile releases. [ABSTRACT FROM AUTHOR]

The restrictions imposed on human activities during the COVID-19 lockdown (LD) period provided a new scenario to identify potential sources of atmospheric pollution. There are several studies that deal with changes in air quality around the world during LD, but very few on atmospheric ammonia (NH 3). Therefore, we examine the changes in global NH 3 during LD (April–May 2020) in comparison to the pre-lockdown (PreLD, April–May 2017–2019) and post-lockdown (PostLD, April–May 2021) periods, and assess the factors responsible for these changes. We observe an increase in NH 3 during LD across the latitudes, with very high values in the western Europe, Eastern China (EC), the Indian subcontinent and the eastern United States of America (USA). However, a decline in NH 3 is observed in some regions of South America (SA) and North America (NA). Similar changes in NH 3 during LD are also observed in smaller spatial scales, as found in 3000 cities across the globe. The reduction of sulphur dioxide (SO 2), nitrogen dioxide (NO 2), nitric acid (HNO 3), humidity and cloud cover as a result of restrictions on human activities, particularly in the western Europe and the USA, may have impeded the conversion of NH 3 to particulates, which led to a higher NH 3 there. Nevertheless, agricultural activities and livestock are the most prominent sources of atmospheric NH 3 , and were not under restriction during LD, show an enhancement in terms of the use of nitrogen fertilizer, crop production and area harvested, which also cause a rise in NH 3 in these regions. Therefore, it is evident that the changes in meteorology and atmospheric composition, together with increased agricultural activities, led to the global increase in atmospheric NH 3 during LD. That is, most pollutants show a decline during LD, but NH 3 exhibits a rise due to its sources such as agricultural activities. Henceforth, environmental regulations, policies and advanced technologies are required in the agricultural sector, such as fertilizer deep placement using urea briquettes and restricted application of nitrogen fertilizers, to curb NH 3 emissions. [Display omitted] • The COVID-19 lockdown helped to understand the agricultural sources of atmospheric NH 3. • Most regions across the globe show an increase in NH 3 owing to rise in agricultural activities. • Changes in meterology and atmospheric composition during lockdown further led to high NH 3. • Policies and advanced technologies are needed in agriculture sector to curb the air pollution. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO 2) plays a critical role in terms of air quality, human health, ecosystems, and its impact on climate change. While the crucial roles of the vertical structure of NO 2 have been acknowledged for some time, there is currently limited knowledge about this aspect in China. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first geostationary satellite instrument capable of measuring the hourly columnar amount of NO 2. The study presented here introduces the use of mixing height for NO 2 in the atmosphere. A thorough examination of spatiotemporal variations in the mixing height of NO 2 was conducted using data from both the GEMS and ground-based air quality monitoring networks. A random forest model based on machine learning techniques was utilized to examine how meteorological parameters affect the mixing height of NO 2. The results of our study reveal a notable seasonal fluctuation in the mixing height of NO 2 , with the highest values observed during the summer and the lowest values during the winter. Additionally, there was an increasing diurnal trend from early morning to mid-afternoon. Moreover, the study discovered elevated NO 2 mixing heights in the dry regions of northern China. The results also indicated a positive correlation between the mixing height of NO 2 and temperature and wind speed, while negative associations were found with relative humidity and air pressure. The machine learning model's predicted NO 2 mixing heights were in good agreement with the measurement-based outcomes, as evidenced by a coefficient of determination (R2) value of 0.96 (0.84 for the 10-fold cross-validation). These findings emphasize the noteworthy influence of meteorological variables on the vertical distribution of NO 2 in the atmosphere and enhance our comprehension of the three-dimensional variations in NO 2. [Display omitted] • Our study comprehensively examined the variations in mixing height of NO 2 in China. • GEMS shows a good potential to monitor the NO 2 pollution hourly. • Meteorological conditions have a significant impact on the mixing height of NO 2. • Machine learning model showed promising results to predict mixing height of NO 2. • Our results enhance our comprehension of the three-dimensional variations in NO 2. [ABSTRACT FROM AUTHOR]