Regional Meteorology Research Articles

Influence of seasonal variation on spatial distribution of PM2.5 concentration using low-cost sensors.

Fine particulate matter (PM2.5) is one of the major airborne pollutants in urban environments and is associated with severe health impacts. In this study, a dense network of low-cost sensor (LCS) is used to cover large spatial area and detect ambient PM2.5 concentration in Guwahati city. The measurements were conducted at multiple sites in different seasons between July 2022 and June 2023. Seasonal variability significantly influences regional meteorology, aerosol optical depth (AOD), and PM2.5 concentration. The seasonal average PM2.5 concentration was highest during winter (113.05µgm -3), followed by post-monsoon (56.11µgm-3), then pre-monsoon (46.60µgm-3), and least for monsoon (32.36µgm-3) season. The elevated PM2.5 concentrations may be attributed to environmental conditions (low ambient temperature, calm wind, and low planetary boundary layer height) that resulted in the least dispersion of PM2.5. The concentration-weighted trajectory (CWT) analysis identifies the effect of regional (Indo-Gangetic Plain and northeast region) and transboundary (Bay of Bengal, Bangladesh, and northwest Asian countries) transported air masses on urban air quality. Post-monsoon and winter season has a high influence on long-range transported aerosols, whereas the monsoon and pre-monsoon seasons are affected by ocean and land air masses. Changes in surrounding activities and meteorology influence spatial distribution of PM2.5 particles. Elevated PM2.5 concentrations were recorded at in-city and outskirt sites because of the nearby activities (industry and traffic) and build-up area. In meteorology, wind significantly affects spatial dispersion of PM2.5 concentration to the sites located in upwind and downwind directions.

Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China

Fine particulate matter (PM2.5) and surface ozone air pollution have severe health consequences. Since China implemented the nationwide Air Pollution Prevention and Control Action Plan (APPCAP) in 2013, annual average PM2.5 concentrations have decreased while O3 concentration have increased in Beijing. It is unclear, however, to the extent that short-term meteorological variability has influenced these trends. To separate the influence of meteorology from long-term emissions-induced changes, we quantify the portion of the long-term PM2.5 and O3 concentration signals associated with short-term meteorological variability. Building off recent literature highlighting the regional nature of pollutant variability, we incorporate both locally observed meteorology and average regional reanalysis. We isolated sub-seasonal variability using the Kolmogorov-Zurbenko filter. Next, we trained and compared the utility of three statistical models of varying complexity (a linear model, a general additive model with splines, and a random forest) in their ability to predict out-of-sample daily concentrations. The random forest model yields the best predictive capability in holdout tests and predicts changing meteorological contributions to PM2.5 and ozone concurrent with changing concentrations across the study period. Results show an overall decrease in meteorology induced PM2.5 concentration variability at daily scales since 2013, but variability from meteorology has increased relative to mean PM2.5 concentrations. In contrast, O3 shows the opposite trend, with increasing meteorology-induced variability (meteorology-induced variability normalized by mean observed O3 has decreased). Our results show the importance of including regional meteorology in explaining local PM2.5 and O3 variability and quantify links between long-term policy implementation and short-term meteorological contributions to pollutant concentrations.

Diurnal Cycle of Precipitation and Extremes in Nepal

This study examines the spatio-temporal distribution of hourly precipitation across Nepal using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data from 2015 to 2021. The findings indicate that hourly precipitation intensity during the monsoon season can reach up to 0.7 mm/hr, while pre-monsoon intensities peak at 0.2 mm/hr. These intensities are predominantly concentrated in mid- and low-elevation areas of central and eastern Nepal. Post-monsoon and winter seasons exhibit high-intensity precipitation patches over high-elevation regions in the western, central, and eastern peripheries of the country. The annual distribution of hourly precipitation shows a pronounced peak during the monsoon season, indicating that a significant portion of the total annual precipitation occurs during this period. Extreme precipitation events follow a similar seasonal distribution, with monsoon extremes exceeding 15 mm/hr. The diurnal cycle of monsoonal precipitation shows unique characteristics, peaking at 0.65 mm/hr around midnight and decreasing to 0.2 mm/hr by late morning, then increasing steadily in the afternoon and evening. Additionally, the study contrasts hourly heavy precipitation extremes (≥10 mm/hr or day) with daily extremes, noting that hourly extremes, despite being accumulated into daily measures, present a higher frequency, and pose greater short-term hazard risks. This analysis over a seven-year period suggests the need for continued research to determine spatial and temporal trends in hourly versus daily extreme precipitation patterns, particularly in the context of climate change and its impacts on regional hydrology and meteorology.

Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms

Abstract. The microprocessor without interlocked piped stages (MIPS) and LoongArch are reduced instruction set computing (RISC) processor architectures, which have advantages in terms of energy consumption and efficiency. There are few studies on the application of MIPS and LoongArch central processing units (CPUs) in geoscientific numerical models. In this study, the Loongson 3A4000 CPU platform with the MIPS64 architecture and the Loongson 3A6000 CPU platform with the LoongArch architecture were used to establish the runtime environment for the air quality modelling system Weather Research and Forecasting–Comprehensive Air Quality Model with extensions (WRF-CAMx) in the Beijing–Tianjin–Hebei region. The results show that the relative errors for the major species (NO2, SO2, O3, CO, PNO3, and PSO4) between the MIPS and X86 benchmark platforms are within ±0.1 %. The maximum mean absolute error (MAE) of major species ranged up to 10−2 ppbV or µg m−3, the maximum root mean square error (RMSE) ranged up to 10−1 ppbV or µg m−3, and the mean absolute percentage error (MAPE) remained within 0.5 %. The CAMx takes about 195 min on the Loongson 3A4000 CPU, 71 min on the Loongson 3A6000 CPU, and 66 min on the Intel Xeon E5-2697 v4 CPU, when simulating a 24 h case with four parallel processes using MPICH. As a result, the single-core computing capability of the Loongson 3A4000 CPU for the WRF-CAMx modelling system is about one-third of the Intel Xeon E5-2697 v4 CPU, and the one of Loongson 3A6000 CPU is slightly lower than that of Intel Xeon E5-2697 v4 CPU; but, the thermal design power (TDP) of Loongson 3A4000 is 40 W, while the TDP of Loongson 3A6000 is 38 W, only about one-fourth of that of Intel Xeon E5-2697 v4, whose TDP is 145 W. The results also verify the feasibility of cross-platform porting and the scientific usability of the ported model. This study provides a technical foundation for the porting and optimization of numerical models based on MIPS, LoongArch, or other RISC platforms.

The multi-year contribution of Indo-China peninsula fire emissions to aerosol radiation forcing in southern China during 2013–2019

Fire emissions in Southeast Asia transported to southern China every spring (March–May), influencing not only the air quality but also the weather and climate. However, the multi-year variations and magnitude of this impact on aerosol radiation forcing in southern China remain unclear. Here, we quantified the multi-year contributions of fire emissions in Indo-China Peninsula (ICP) region to aerosol radiation forcing in the various southern Chinese provinces during the fire season (March–May) of 2013–2019 combining the 3-dimension chemical transport model and the Column Radiation Model (CRM) simulations. The models' evaluations showed they reasonably capture the temporal and spatial distribution of surface aerosol concentrations and column aerosol optical properties over the study regions. The fire emissions over the ICP region were found to increase the aerosol optical depth (AOD) value by 0.1 (15 %) and reduce the single scattering albedo (SSA) in three southern regions of China (Yunnan-YN, Guangxi-GX, and Guangdong-GD from west to east), owing to increases in the proportions of black carbon (BC, 0.4 % ± 0.1 %) and organic carbon (OC, 3.0 % ± 0.9 %) within the aerosol compositions. The transported smoke aerosols cooled surface but heated the atmosphere in the southern China regions, with the largest mean reduction of −5 Wm−2 (−3 %) in surface shortwave radiation forcing and the maximum daily contributions of about −15 Wm−2 (−15 %) to the atmosphere radiation forcing in the GX region, followed by the GD and YN regions. The impacts of ICP fire emissions on aerosol optical and radiative parameters declined during 2013–2019, with the highest rate of 0.393 ± 0.478 Wm−2 yr−1 in the GX for the shortwave radiation forcing in the atmosphere. Besides, their yearly changes in the contribution were consistent with the annual fire emissions in the ICP region. Such strong radiative perturbations of ICP fire emissions were expected to influence regional meteorology in southern China and should be considered in the climate simulations.

An Assessment of Geological Hazard Management using Dynamically Stabilized Recurrent Neural Network and Beluga Whale Optimization Algorithm

Mountainous region geological hazards are a leading cause of natural disasters, resulting in significant human and economic losses. Regional topography, landforms, lithology, plant life, geological circumstances, and meteorology all have a significant impact on their creation. Gansu, situated in the interior of Northwestern China, features Lanzhou as its capital and primary urban center, positioned in the southeast of the province. Geological risks, particularly landslides, mudslides, and avalanches, present significant challenges to Gansu Province. Consequently, local authorities are actively devising customized strategies to mitigate these hazards and foster sustainable development. In this research work, an Assessment of Geological Hazard Management using Dynamically Stabilized Recurrent Neural Network and Beluga Whale Optimization Algorithm (AGL-HM-DSRNN-BWOA) is proposed. Initially, the input raster data are gathered from the Normalised Difference Vegetation Index (NDVI) dataset. The input raster data is then pre-processed using Adaptive Actor-Critic Bilateral Filter (A2CBF) to reduce noises and increase the overall quality of the raster data. To classify the geological hazards, the pre-processed raster data are fed into a neural network named DSRNN. The geological hazard is accurately categorized as low risk, medium-low risk, medium-high risk, high risk using proposed DSRNN. In general, DSRNN does not express some adaption of optimization strategies for determining optimal parameters to promise exact classification for managing geological hazard by assessment. Therefore, Beluga Whale Optimization Algorithm (BWOA) is proposed to enhance weight parameter of DSRNN classifier, which precisely assess for managing the geological hazards. The efficiency of the proposed AGL-HM-DSRNN-BWOA approach is evaluated using a number of performance criteria, including accuracy, sensitivity, specificity, ROC, mean square error, root mean square error, mean absolute error. The proposed AGL-HM-DSRNN-BWOA method attains 22.36%, 25.42% and 18.17% higher accuracy, 21.26%, 15.42% and 19.27% higher sensitivity, 28.36%, 25.32% and 28.27% higher F-measure compared with existing methods, such as the Risk assessment and its influencing factors examination of geological hazards in typical mountain environment (RA-IFA-GH-TME), Feasibility study of land cover categorization under normalized difference vegetation index for landslide risk assessment(LCC-NDVI-LRA), and Multiple hazard exposure mapping under machine learning for Salzburg, Austria (MH-EM-SSA-ML) respectively.

Simulation of ozone–vegetation coupling and feedback in China using multiple ozone damage schemes

Abstract. As a phytotoxic pollutant, surface ozone (O3) not only affects plant physiology but also influences meteorological fields and air quality by altering leaf stomatal functions. Previous studies revealed strong feedbacks of O3–vegetation coupling in China but with large uncertainties due to the applications of varied O3 damage schemes and chemistry–vegetation models. In this study, we quantify the O3 vegetation damage and the consequent feedbacks to surface meteorology and air quality in China by coupling two O3 damage schemes (S2007 vs. L2013) into a fully coupled regional meteorology–chemistry model. With different schemes and damaging sensitivities, surface O3 is predicted to decrease summertime gross primary productivity by 5.5 %–21.4 % and transpiration by 5.4 %–23.2 % in China, in which the L2013 scheme yields 2.5–4 times of losses relative to the S2007 scheme. The damage to the photosynthesis of sunlit leaves is ∼ 2.6 times that of shaded leaves in the S2007 scheme but shows limited differences in the L2013 scheme. Though with large discrepancies in offline responses, the two schemes yield a similar magnitude of feedback to surface meteorology and O3 air quality. The O3-induced damage to transpiration increases national sensible heat by 3.2–6.0 W m−2 (8.9 % to 16.2 %), while reducing latent heat by 3.3–6.4 W m−2 (−5.6 % to −17.4 %), leading to a 0.2–0.51 °C increase in surface air temperature and a 2.2 %–3.9 % reduction in relative humidity. Meanwhile, surface O3 concentrations on average increase by 2.6–4.4 µg m−3, due to the inhibitions of stomatal uptake and the anomalous enhancement in isoprene emissions, the latter of which is attributed to the surface warming by O3–vegetation coupling. Our results highlight the importance of O3 control in China due to its adverse effects on ecosystem functions, global warming, and O3 pollution through O3–vegetation coupling.

PV to reduce evaporative losses in the channels of the São Francisco’s River water transposition project

Open water transposition channels in hot and arid regions, like those in the São Francisco River Integration Project (PISF) in Brazil, suffer significant water losses through evaporation. This paper proposes covering these channels with photovoltaic (PV) panels to reduce evaporation while simultaneously generating clean energy. The research aims to quantify water savings and energy generation potential across all channel lengths and assess whether the generated solar power can substitute grid electricity for powering the transposition pumps during peak hours, thereby enhancing energy efficiency. This study analyzed the state-of-the-art of PV generation and calculated their solar potential. Identified the specific characteristics of PISF channels and watercourses considering the regional geography, meteorology, irradiation, and social peculiarities. And, finally, assessed the feasibility of covering the watercourses with solar panels. The results reveal that covering all current PISF channels with PV panels could save up to 25,000 cubic meters of water per day, significantly contributing to water security and improving the quality of life for the local population. Additionally, the project could generate 1200 gigawatt-hours of electricity annually, meeting the energy demands of the transposition pumps during peak hours and promoting energy efficiency within the project. This research paves the way for utilizing PV technology to address water scarcity challenges and enhance the sustainability of water infrastructure projects in arid regions worldwide.

Molecular composition and the impact of fuel moisture content on fresh primary organic aerosol emissions during laboratory combustion of Ponderosa pine needles.

Pine needles represent an important fuel source in coniferous forest systems in the western United States. During forest fires, they can be easily ignited and help sustain flame on the ground. In this study, a comprehensive chemical analysis was conducted to examine oxygenated organic compounds (OOCs) present in PM2.5 formed from burning dry and moist ponderosa pine needles (PPN) in the presence and absence of fine woody debris (FWD). The effect of fuel moisture content (FMC), a key parameter that influence smoke formation, has not received much attention. Therefore, we also investigated the effect of FMC on PM2.5 formation and its composition. Thirty three experiments were conducted at the US Forest Service Fire Science Laboratory. PM2.5 was collected onto 47 mm Teflon filters, and silylated extracts were analyzed by gas chromatography-mass spectrometry. More than fifty OOCs were identified, including levoglucosan and mannosan; n-dodecanoic acid and n-hexadecanoic acid; dihydroabietic acid, and dehydroabietic acid; and a series of intermediate volatile and semivolatile organic compounds. Mass spectra of a wide variety of compounds in electron and chemical ionization mode are provided. Most of these OOCs were identified in this study for the first time in PPN aerosol, although some were previously reported in pine wood and other biomass burning aerosol. Our results show significant changes in the composition and abundance of particles depending on the amount and type of PPN burned. When compared with dry PPN condition, moist PPN showed decreased emissions of PM2.5 and OOCs, due likely to the presence of water in the system that partially suppressed the production of OOCs. Incorporating pine needles in atmospheric models as a contributor to smoke particles generated during forest fires is an essential step towards reducing the current uncertainties regarding the influence of these aerosols on chemical/air mass characteristics, regional meteorology, and the climate.

Sea Wave Energy Simulation of Oscillating Water Column (OWC) Technology, Binongko Island, Wakatobi, Southeast Sulawesi

Indonesia is a country that has the largest sea area of several countries spread across the world. This is an advantage for Indonesia in utilizing the energy potential of the seas and oceans. One of which is the potential of Oscillating Water Column (OWC) Technology, to utilize the energy potential of the seas and oceans not only requires sea potential but also must have location parameters with suitable seabed topography. gentle and constant sea wave height, usually influenced by wind speed. One area of Indonesia that has these parameters is Binongko Island, Wakatobi, Southeast Sulawesi, whose wave characteristics are a basic reference for exploring the enormous energy potential of the seas and oceans in that location. The author uses a simulation model with the Graphical User Interface (GUI) method in the Matlab program to determine the potential energy produced. The data used in this research uses data analysis from the Southeast Sulawesi Regional Meteorology, Climatology, and Geophysics Agency using data samples for 2021 and 2022. The data used includes sea wave height data and sea wave period data at the research location in 2021 and the 2022 time period. Through this data, it is known that Binongko Island, Wakatobi, Southeast Sulawesi 2021 will have minimum sea wave height. of 0.5125 meters and a maximum value of 1.04167 meters, for the minimum sea wave period of 2.34993 seconds and a maximum value of 3.57008 seconds, while in 2022 the minimum sea wave height will be 0.341667 meters and the maximum value will be 0.895833 meters, for a minimum sea wave period of 1.91045 and a maximum value of 3.30481 seconds. With this data, Binongko Island, Wakatobi, Southeast Sulawesi in 2021 will be able to produce a minimum of 20.8844 watts and a maximum of 75.7116 watts, while in 2022 it will be able to produce a minimum of 12.7715 watts and a maximum of 43.5652 watts.

Characterization of urban aerosol pollution before and during the COVID-19 crisis in a central-eastern European urban environment

The worldwide restrictions due to the COVID-19 pandemic induced a radical change in urban air quality. The objective of this work was to study and understand the variation of PMcoarse and PM2.5 pollution besides other air quality (AQ) parameters in the city of Debrecen, Hungary from 2018 to 2022. To achieve this goal, we introduced an integrated approach that combines source apportionment by receptor modelling, trajectory-based statistical methods and takes into account local and regional meteorology. Concentration, elemental composition, and sources of atmospheric particulate matter (APM) were determined for four lockdown periods with varying levels of restrictions, two transition intervals and two relaxation periods in 2020-22, and they were compared to corresponding baseline values from 2018-19. The concentration, composition and sources of PM2.5, PMcoarse showed strong seasonality. The main component of PMcoarse was mineral dust (32%), while BC (15%), sulphate aerosols (19%) and mineral dust (13%) were the major constituents of PM2.5. Source apportionment by Positive Matrix Factorization identified 8 sources on both size fractions: two types of soil, traffic, combustions, biomass burning, biogenic emission, sea salt, construction, roadworks, secondary sulphate. The relative contribution of sources did not change during the pandemic compared to the previous two years. However, considering the whole 2-year-long period affected by restrictions, a 20–25% reduction was detected in the concentrations and source contributions, apart from temporary sources like roadworks or sea salt episodes. The changed habits of the population could be traced in the evolution of APM. Before the pandemic, increased concentration of traffic related pollutants was characteristic of the school year, while in 2020-21 it was more typical in the summers, the relaxation periods. On the other hand, staying at home during lockdowns led to a significant increase in biomass burning from domestic heating in spring 2020 and 2021. By studying the individual lockdown and relaxation periods, we have shown that the evolution of APM pollution in relatively short, 2-3-month intervals was significantly influenced by local and regional meteorological parameters, the origin of air masses, desert dust episodes and periodic construction works.

Solar channels as an innovative energy approach for large water transposition projects

Open water transposition channels in hot and arid regions lose significant amounts of water through evaporation. This is the case with the São Francisco River Integration Project (PISF) in Brazil. This paper proposes using photovoltaic (PV) panels to cover the channels of the PISF to reduce evaporation and save water. The study aims to evaluate the potential amount of water saved by this technology across all channel lengths, as well as the energy that can be generated by the covered watercourses. The study also examines whether the energy produced can be used to power the transposition pumps during peak radiation hours, improving the energy efficiency of the project. To achieve the study’s goals, the authors conducted a thorough review of academic and industrial literature on the state-of-the-art of PV generation in Brazil and worldwide. They also examined the regional geography, meteorology, irradiation, and social characteristics of the area. The authors identified the characteristics of the channels and watercourses of PISF and calculated their solar potential. The study found that covering all current channel extensions with PV panels could save up to 25,000 m3Water per day to supply the deprived population, improving their quality of life and generating 1,250 GWh annually.

All-weather precipitable water vapor map reconstruction using data fusion and machine learning-based spatial downscaling

Precipitable water vapor (PWV) detection with high spatial resolution and high accuracy is of significant importance for contributing to extreme weather events monitoring and forecasting. Current PWV products, however, suffer from limitations of spatial and temporal discontinuities, low accuracy, and coarse spatial resolution. To overcome this problem, a data fusion and machine learning-based spatial downscaling solution is proposed. At first, spatially complete PWV maps are generated by integrating calibrated PWV of Moderate Resolution Imaging Spectroradiometer (MODIS) and the ERA5 PWV data from 2018 to 2022. Subsequently, three spatial downscaling models based on Gradient Boosting Decision Tree (GBDT), Multi-layer Perceptron Neural Network (MLPNN), and Random Forest (RF), respectively, are developed to produce high-quality, all-weather PWV considering the land-cover type. It has been verified that the high-quality all-weather PWV maps generated by the GBDT, MLPNN, and RF models exhibit strong agreement with Global Navigation Satellite System (GNSS) PWV estimates. The correlation coefficients are 0.95, 0.87, and 0.87, while the overall Bias is 0.29 mm, 0.67 mm, and 0.35 mm, and the root mean square errors (RMSE) are 1.74 mm, 2.98 mm, and 3.06 mm, respectively. These results significantly enhance the accuracy of MODIS PWV products (R2 = 0.73, RMSE = 5.64 mm, Bias = 3.05 mm). Notably, the GBDT model outperforms the other models in terms of performance. Compared to MODIS PWV, the new PWV map with a data fusion and machine learning-based spatial downscaling approach effectively utilizes the advantage of satellite-based and reanalyzed PWV products, providing continuous, detailed, and reasonable variation in time and space. Moreover, it is less influenced by seasonal changes. The new PWV map has a promising application for regional hydrology and meteorology.

A roadmap to estimating agricultural ammonia volatilization over Europe using satellite observations and simulation data

Abstract. Ammonia (NH3) is one of the most important gases emitted from agricultural practices. It affects air quality and the overall climate and is in turn influenced by long-term climate trends as well as by short-term fluctuations in local and regional meteorology. Previous studies have established the capability of the Infrared Atmospheric Sounding Interferometer (IASI) series of instruments, aboard the Metop satellites, to measure ammonia from space since 2007. In this study, we explore the interactions between atmospheric ammonia, land and meteorological variability, and long-term climate trends in Europe. We investigate the emission potential (Γsoil) of ammonia from the soil, which describes the soil–atmosphere ammonia exchange. Γsoil is generally calculated in-field or in laboratory experiments; here, and for the first time, we investigate a method which assesses it remotely using satellite data, reanalysis data products, and model simulations. We focus on ammonia emission potential in March 2011, which marks the start of growing season in Europe. Our results show that Γsoil ranges from 2 × 103 to 9.5 × 104 (dimensionless) in fertilized cropland, such as in the North European Plain, and is of the order of 10–102 in a non-fertilized soil (e.g., forest and grassland). These results agree with in-field measurements from the literature, suggesting that our method can be used in other seasons and regions in the world. However, some improvements are needed in the determination of mass transfer coefficient k (m s−1), which is a crucial parameter to derive Γsoil. Using a climate model, we estimate the expected increase in ammonia columns by the end of the century based on the increase in skin temperature (Tskin), under two different climate scenarios. Ammonia columns are projected to increase by up to 50 %, particularly in eastern Europe, under the SSP2-4.5 scenario and might even double (increase of 100 %) under the SSP5-8.5 scenario. The increase in skin temperature is responsible for a formation of new hotspots of ammonia in Belarus, Ukraine, Hungary, Moldova, parts of Romania, and Switzerland.

Potential Impact of Battery Electric Vehicle Penetration and Changes in Upstream Process Emissions Assuming Night‐Charging on Summer O3 Concentrations in Japan

AbstractA regional meteorology–chemistry model was used to evaluate the impact of battery electric vehicles (BEV) penetration on summer O3 concentrations in Kanto (Japan's most populous region). When all passenger cars shifted to BEVs, daytime ozone (O3) concentrations decreased over a wide area. The reduction of vehicle exhaust reduced O3 in inland suburban areas (NOx‐limited) and the reduction of gasoline fuel evaporation from vehicles and gas stations reduced O3 in urban areas (volatile organic compound (VOC)‐limited). The maximum location of maximum daily 8‐hr average O3 (MDA8hO3) sensitivity (−5%; −5 ppb) was at midway between urban and suburban areas, to which the reduction of exhaust and fuel evaporation contributed equally, respectively. The additional emissions from thermal power plants due to BEV's night‐charging could cause up to ±5% sensitivity to next‐day surface O3 concentrations topically. Depending on the O3 sensitivity regime (NOx‐ or VOC‐limited), additional NOx‐rich plumes from rural (urban) power plants tended to increase (decrease) the next day's O3. However, the spatial distribution of the regime varies temporally depending on such as NOx/VOC emission ratios and meteorological conditions. This study indicated that the distribution of negative (positive) O3 sensitivity by NOx‐rich plume is consistent with that of small (large) H2O2/HNO3 concentration ratios, indicating that H2O2/HNO3 works well as an indicator to discriminate O3 sensitivity regimes. Utilization of the H2O2/HNO3 indicator would enable regime distribution predictions without sensitivity simulations with varying NOx and VOC emissions, which would contribute to reducing computational costs.

Retrieval uncertainty and consistency of Suomi-NPP VIIRS Deep Blue and Dark Target aerosol products under diverse aerosol loading scenarios over South Asia

Retrieval accuracy and stability of two operational aerosol retrieval algorithms, Deep Blue (DB) and Dark Target (DT), applied on Visible Infrared Imaging Radiometer Suite (VIIRS) on-board Suomi National Polar-orbiting Partnership (S-NPP) satellite were evaluated over South Asia. The region is reported to be highly challenging to accurate estimation of satellite-based aerosol optical properties due to variations in surface reflectance, complex aerosol system and regional meteorology. Performance of both algorithms were initially evaluated by comparing their ability to retrieve aerosol signal over the complex geographical region under specific air pollution emission scenario. Thereafter, retrieval accuracy was investigated against 10 AERONET sites across South Asia, selected based on their geography and predominance aerosol types, from year 2012–2021. Geo-spatial analysis indicates DB to efficiently retrieve fine aerosol features over bright arid surfaces, and for smoke/dust dominating events whereas DT was better to identify small fire events under dark vegetated surface. Both algorithms however, indicate unsatisfactory retrieval accuracy against AERONET having 56–59% of valid retrievals with high RMSE (0.30–0.33) and bias. Overall, DB slightly underpredicted AOD with −0.02 mean bias (MB) whereas DT overpredicted AOD (MB: 0.13), with seasonality in their retrieval efficiency against AERONET. Time-series analysis indicates stability in retrieving AOD and match-up number for both algorithms. Retrieval bias of DB and DT AOD against AERONET AOD under diverse aerosol loading, aerosol size, scattering/absorbing aerosol, and surface vegetation coverage scenarios revealed DT to be more influenced by these conditions. Error analysis indicates at low AOD (≤0.2), accuracy of both DB and DT were subject to underlying vegetation coverage. At AOD>0.2, DB performed well in retrieving coarse aerosols whereas DT was superior when fine aerosols dominated. Overall, accuracy of both VIIRS algorithms require further refinement to continue MODIS AOD legacy over South Asia.

Relationship between light absorption properties of black carbon and aerosol origin at a background coastal site

As a potent climate forcer, black carbon (BC) optical properties can have significant impacts on the regional meteorology and climate. To unveil the seasonal differences of BC and its contribution by various emission sources, a one-year continuous monitoring of atmospheric aerosols was conducted at a background coastal site in Eastern China. By comparing the seasonal and diurnal patterns between BC and elemental carbon, we observed that BC were evidently aged with varying extents among all four seasons. The light absorption enhancement of BC (Eabs) was calculated as 1.89 ± 0.46, 2.40 ± 0.69, 1.91 ± 0.60, and 1.34 ± 0.28, from spring to winter, respectively, indicating that BC was more aged in summer. Contrary to the negligible impact of pollution levels on Eabs, the patterns of air masses arriving to the sampling site had a significant impact on the seasonal optical characteristics of BC. Sea breezes evidently exhibited higher Eabs than land-sourced breezes, and BC was more aged and light-absorbing with an increased contribution of marine airflows. By applying a receptor model, we resolved six emission sources as ship emission, traffic emission, secondary pollution, coal combustion, sea salt, and mineral dust. The mass absorption efficiency of BC for each source was estimated, showing the highest from the ship emission sector. This explained the highest Eabs observed in summer and sea breezes. Our study highlights that curbing emission from shipping activities is beneficial for reducing the warming effect of BC in coastal areas, particularly in the context of future rapid development of international shipping.

Development of an Automated Temperature Calibration Monitoring System Using Internet of Things for the Regional Meteorology, Climatology, and Geophysics Agency (Bmkg) in Medan

The Regional Meteorology, Climatology, and Geophysics Agency (BMKG) plays a crucial role in providing accurate and reliable services related to meteorology, climatology, and geophysics. Temperature observation is one of the important tasks carried out by the BMKG as it is essential for weather and climate forecasting, as well as for predicting natural disasters. To ensure the accuracy of the data, the thermometers used for temperature observation must be in good working condition and calibrated regularly. According to the Republic of Indonesia Law No. 31, Article 48, Year 2009 on Meteorology, Climatology, and Geophysics (MKG), all observation equipment must be in good working condition and calibrated regularly. Calibration is a crucial step in ensuring the accuracy and operational fitness of the observation equipment. The International Organization for Standardization (ISO) / International Electrotechnical Commission (IEC) 17025:2017 also emphasizes the importance of ensuring the quality and accuracy of all measurement instruments. The Calibration Laboratory at the BMKG Regional Office I in Medan is accredited with ISO/IEC 17025:2017 by the National Accreditation Committee (KAN). However, the calibration process can be time-consuming and requires constant monitoring to achieve stable data. During temperature and humidity calibration, the calibration laboratory's environment must be conditioned to maintain the performance of sensitive instruments that are susceptible to environmental changes. This study aims to design an automated temperature calibration monitoring system using the Internet of Things (IoT) to improve the efficiency of the calibration process and achieve maximum calibration results at the BMKG Regional Office I in Medan. The system will enable the calibration personnel to monitor the calibration process remotely and receive real-time data, allowing for more effective analysis and decision-making.

Effects of Elevated Ozone Exposure on Regional Meteorology and Air Quality in China Through Ozone‐Vegetation Coupling

AbstractOzone is a phytotoxic pollutant that could damage the vegetation growth and lead to complicated impacts on air quality through meteorological and biogeochemical feedbacks. This study implements a semi‐empirical parameterization regarding the impacts of ozone exposure on photosynthesis rate and stomatal resistance into the Noah‐Multi‐parameterization (Noah‐MP) dynamic vegetation module of Weather Research and Forecasting with Chemistry (WRF/Chem) model. The gaseous dry deposition and biogenic emission algorithms are also coupled with Noah‐MP to enable the ozone‐vegetation coupling. This model reproduces the near‐surface meteorology, air pollutants and vegetation physiology in China, with a spatial correlation coefficient more than 0.9 and normalized mean bias from −0.19 to 0.42. The optimized model also improves the simulations of vegetation physiology (e.g., a reduction of model error by 18%–32%) and ozone dry deposition velocity. The elevated ozone damages plant photosynthesis, and decreases the national gross primary productivity (−28.85%) and leaf area index (−17.41%). The plant transpiration and surface heat flux, as well as air temperature (e.g., up to +0.16°C in summer) and other associated meteorological variables are also altered, finally contributing to 0.49 μg m−3 increase of surface ozone. Otherwise, the suppressed vegetation LAI and biogenic emissions, as well as the lower dry deposition velocity in response to the ozone‐vegetation coupling contribute to the remaining ozone changes by −1.07 μg m−3 and 1.18 μg m−3, jointly constituting the complicated ozone‐vegetation feedbacks on air quality. Our results highlight the necessity of including the ozone‐vegetation coupling in models for reliable prediction of regional climate and air quality.

Impacts of land use and land cover changes on local meteorology and PM2.5 concentrations in Changchun, Northeast China

Land use and land cover (LULC) changes have a considerable influence on the surface energy balance, altering regional meteorology and air quality. However, this impact is not quantified in Changchun, an important city in the old industrial base of Northeast China. In this study, based on the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model, the LULC2017 (LULC data in 2017) and LULC2001 (LULC data in 2001) scenarios were simulated for January and July 2017, respectively, to assess the impact of LULC changes on meteorology and fine particulate matter (PM2.5) concentrations in Changchun. The results show that the sensible heat flux in the urban expansion area (UEA) increased during the daytime, reaching a maximum value of 154 W/m2 and 162 W/m2, respectively, while the latent heat flux decreased during the daytime, reaching a maximum value of 22.84 W/m2 and 180.75 W/m2, respectively. Consequently, 2 m temperature (T2) increased by 4 °C and 3 °C, respectively; 10 m wind speed (WS10) increased by 1.05 m/s and 1.60 m/s, respectively; and planetary boundary layer height (PBLH) increased by 100 m and 117 m, respectively. These variations in meteorological factors can substantially impact the spatial distribution of air pollutants. In the UEA, PM2.5 concentrations decreased by 34 μg/m3 and 20 μg/m3 in January and July, respectively. The change in SO42− accounted for approximately 25% of the total concentration change of PM2.5, with a decrease of approximately 5–6 μg/m3 during the nighttime in January. Secondary organic aerosol (SOA) formed from biogenic volatile organic compounds (BVOC) precursors (BSOA) slightly decreased owing to the reduction in croplands dominated by green vegetation. Meanwhile, PM2.5 concentrations in the surrounding areas of the UEA increased significantly in January. The results of the process analysis based on the CMAQ model indicate that the main reason for the spatial variation of PM2.5 concentrations is the enhancement of transport and diffusion in the horizontal and vertical directions in the UEA. In January, the negative contribution of vertical advection (ZADV) and horizontal advection (HADV) processes to PM2.5 in the UEA increased by 25 μg/m3 and 40 μg/m3, respectively. Vertical diffusion (VDIF) process caused an increase in PM2.5 diffusion by 40 μg/m3 and 16 μg/m3 during the daytime and nighttime in the UEA, respectively. In July, the negative contribution of VDIF and HADV processes to PM2.5 increased by 40 μg/m3 and 32 μg/m3 during the nighttime in the UEA, respectively.