Meteorological Drivers Research Articles

Spatiotemporal variations of wintertime secondary PM2.5 and meteorological drivers in a basin region over Central China for 2015–2020

It is important to comprehend the contribution of secondary PM2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) to ambient PM2.5 for the long-term change of air quality. This study used the approximate envelope method to identify the variations of primary and secondary PM2.5 concentrations based on routine observations of environment and meteorology in the Twain-Hu Basin (THB, located in Central China), during the winters of 2015–2020. It is found that the contribution of secondary PM2.5 to ambient PM2.5 was enhanced with accompanying the descending trend in PM2.5 concentrations over the recent years. The regional average for secondary PM2.5 contributions was 62.26% in the THB during winters of 2015–2020, and the high secondary contributions were centered in the THB urban areas and major transport routes in the northwestern region. Secondary PM2.5 contributions averaged over the THB increased from 50.41% in clean air quality to 77.09% in heavy PM2.5 pollution with a significant contribution of secondary aerosols to heavy pollution. The secondary PM2.5 contributions presented two peaks at 3:00–7:00 a.m. and 13:00–17:00 p.m., which could be attributed respectively to the nighttime high relative humidity and daytime high air temperature. Regional transport of gaseous precursors of aerosols could lead to the increasing secondary PM2.5 levels in the strong oxidizing atmosphere.

Typhoon-associated air quality over the Guangdong–Hong Kong–Macao Greater Bay Area, China: machine-learning-based prediction and assessment

Abstract. The summertime air pollution events endangering public health in the Guangdong–Hong Kong–Macao Greater Bay Area are connected with typhoons. The wind of the typhoon periphery results in poor diffusion conditions and favorable conditions for transboundary air pollution. Random forest models are established to predict typhoon-associated air quality in the area. The correlation coefficients and the root mean square errors in the air quality index (AQI) and PM2.5, PM10, SO2, NO2 and O3 concentrations are 0.84 (14.88), 0.86 (10.31 µg m−3), 0.84 (17.03 µg m−3), 0.51 (8.13 µg m−3), 0.80 (13.64 µg m−3) and 0.89 (22.43 µg m−3), respectively. Additionally, the prediction models for non-typhoon days are established. According to the feature importance output of the models, the differences in the meteorological drivers of typhoon days and non-typhoon days are revealed. On typhoon days, the air quality is dominated by local source emission and accumulation as the sink of pollutants reduces significantly under stagnant weather, while it is dominated by the transportation and scavenging effect of sea breeze on non-typhoon days. Therefore, our findings suggest that different air pollution control strategies for typhoon days and non-typhoon days should be proposed.

Characterization of the short-term temporal variability of road dust chemical mixtures and meteorological profiles in a near-road urban site in British Columbia

ABSTRACT Springtime road dust is a common air quality concern for northern latitudes communities. Traction material, brake wear, tire wear, and other particles entrained in snow during winter become re-suspended into the air as the snow melts. Characterization of within-season variability of road dust composition and weather conditions is critical to understanding its acute health impacts. Sixty 24-hour samples of PM10 were collected with a high-volume sampler at a near-road site in Prince George, British Columbia. Near-road samples were collected intermittently in winter and daily in spring. All samples were analyzed for 18 trace elements. We used data from a nearby regulatory monitor to estimate daily coarse fraction (PM10–2.5). Proportion of PM10–2.5 in PM10 was used to classify days as high or low road dust based on a threshold of 65% PM10–2.5. We identified clusters of near-road samples based on weather parameters and PM10 components using Bayesian profile regression (BPR). Twenty-one near-road PM10 samples were classified as high road dust days and 34 as low road dust days. Concentrations of total trace elements and specific trace elements were significantly higher on high road dust days (aluminum, chromium, iron, lead, tin, vanadium, and zinc). High road dust days also had low relative humidity and precipitation and higher temperature and air pressure. We identified five clusters of near-road PM10 mixtures that suggested an interaction between temperature and humidity for road dust impacts. Samples from a near-road site indicated days highly affected by springtime road dust are distinct from other days based on particulate mixtures and meteorological factors. Higher loading of trace element mixtures in PM10 on high road dust days has important implications for acute toxicity of road dust. Relationships between road dust and weather may facilitate further research into health effects of road dust as the climate changes. Implications: Non-tailpipe emissions driven by springtime road dust in northern latitude communities is increasing in importance for air pollution control and improving our understanding of the health effects of chemical mixtures from particulate matter exposure. High-volume samples from a near-road site indicated that days affected by springtime road dust are substantively different from other days with respect to particulate matter mixture composition and meteorological drivers. The high load of trace elements in PM10 on high road dust days has important implications for the acute toxicity of inhaled air and subsequent health effects. The complex relationships between road dust and weather identified in this study may facilitate further research on the health effects of chemical mixtures related to road dust while also highlighting potential changes in this unique form of air pollution as the climate changes.

Long-term changes in macroinvertebrate communities across high-latitude streams.

Long-term records of benthic macroinvertebrates in high-latitude streams are essential for understanding climatic changes, including extreme events (e.g. floods). Data extending over multiple decades are typically scarce. Here, we investigated macroinvertebrate community structural change (including alpha and beta diversity and gain and loss of species) over 22 years (1994-2016) in 10 stream systems across Denali National Park (Alaska, USA) in relation to climatological and meteorological drivers (e.g. air temperature, snowpack depth, precipitation). We hypothesised that increases in air temperature and reduced snowpack depth, due to climatic change, would reduce beta and gamma diversity but increase alpha diversity. Findings showed temporal trends in alpha diversity were variable across streams, with oscillating patterns in many snowmelt- and rainfall runoff-fed streams linked to climatic variation (temperature and precipitation), but increased over time in several streams supported by a mixture of water sources, including more stable groundwater-fed streams. Beta-diversity over the time series was highly variable, yet marked transitions were observed in response to extreme snowpack accumulation (1999-2000), where species loss drove turnover. Gamma diversity did not significantly increase or decrease over time. Investigating trends in individual taxa, several taxa were lost and gained during a relative constrained time period (2000-2006), likely in response to climatic variability and significant shifts in instream environmental conditions. Findings demonstrate the importance of long-term biological studies in stream ecosystems and highlight the vulnerability of high-latitude streams to climate change.

Summertime Characteristics of Atmospheric Polycyclic Aromatic Hydrocarbons in a Coastal City of Northern Poland.

Parent polycyclic aromatic hydrocarbons (PAHs) in the gas and particle fraction were measured between May and August 2021 at a coastal urban site in Poland, to examine their chemical characteristics, distribution, sources, deposition fluxes and interactions with basic meteorological drivers. The mean concentration of PAHs in the gas phase was significantly higher (26.26 ± 15.83 ng m-3) than levels measured in the particle phase (1.77 ± 1.26 ng m-3). The highest concentration in the gas phase was found for phenanthrene (Phe), followed by fluoranthene (Flt), acenaphthene (Ace) and naphthalene (Naph). The contribution from each group of PAHs to the total particulate phase accounted for 50%, 25%, 14% and 12% for 3-, 4-, 5- and 6-ring compounds, respectively. The mean ΣPAH deposition flux was 59 ± 24 ng m-2 day-1. During the whole field campaign, the efficient removal of PM-bound PAHs was typically observed after precipitation events. Based on statistical analysis, it was found that 4-ring PAHs were less effectively removed (25%) by daily precipitation as compared to 5- and 6-ring components, whose fluxes decreased by 32% and 53%, respectively. This study revealed local urban sources such as vehicular emissions, coal-fired power plants, shipping activities, docks/ports infrastructure and municipal solid waste recycling units as predominant contributors to PM-bound and gas-phase PAHs.

Groundwater recharge in northern New England: Meteorological drivers and relations with low streamflow

AbstractMeteorological drivers of groundwater recharge for spring (February–June), fall (October–January), and recharge‐year (October–June) recharge seasons were evaluated for northern New England and upstate New York from 1989 to 2018. Monthly groundwater recharge was computed at 21 observation wells by subtracting the water levels at the end of each month from the level of the previous month; only positive monthly values were used to compute seasonal recharge. Precipitation, temperature, sea‐level pressure, 500‐mb geopotential heights, and various teleconnection indices were tested as explanatory variables for the interannual variability of recharge using random forest machine learning models. Precipitation within recharge seasons was positively correlated with groundwater recharge for most wells in all seasons. In general, whilst groundwater recharge in the study area was generally highest during the months of March and April, October precipitation was an important month for explaining the interannual groundwater recharge variability. This is likely because the variability in recharge in October may be high or low for given years. Sea‐level pressure and 500‐mb heights were typically inversely correlated with groundwater recharge during the recharge‐year and fall recharge seasons, as higher sea‐level pressure and heights are usually associated with clearer skies and less precipitation. The North Atlantic Oscillation, Pacific‐North American pattern, and Pacific Decadal Oscillation teleconnections affected groundwater recharge differently by well and season. The influence of groundwater recharge on minimum daily streamflows during the subsequent summer/fall was also analysed. Summer precipitation was the most important explanatory variable for study streams whilst groundwater recharge and summer air temperature were significant variables for a few streams.

Higher atmospheric evapotranspiration demand intensified drought in semi‐arid sandy lands, northern China

AbstractDrought seriously endangers the development of agro‐pastoral ecotone in arid and semi‐arid regions. However, drought evolution characteristics of semi‐arid sandy grassland and its drivers are not well understood in the complex climate change context. Thus, in this paper, the revised standardized precipitation evaporation indices on one‐month (SPEI‐1) and growing‐season (SPEI‐6) scale, flash drought and dry spells (DS) were calculated as drought indicators, and the temporal variabilities and meteorological drivers of drought in Horqin Sandy Land during the growing period (April–September) from 2007 to 2021 were studied. The results showed that SPEI‐1 and SPEI‐6 exhibited negative trends and periodic change. This drought aggravation was manifested in increased grades and numbers of drought months. Flash droughts can occur every month during the growing season. And its occurrence was not only positively correlated with all drought events in frequency but also synchronized in time. The increased number of DS showed an asymmetrical shift towards 7‐day‐and‐longer DS. SPEI sensitivity to precipitation (P) was noticeably higher than to atmospheric evapotranspiration demand (AED) at both monthly and growing season scales. SPEI sensitivity to AED was modulated by P amounts, which was higher during low P period. Apart from the amounts of P and AED, their temporal variability also strongly determined how they affect the drought severity. P was also the main driver for flash drought and long DS, but AED with larger amount and higher variability is the reason for their aggravation. Our results demonstrate the complexity of drought intensification in semi‐arid sandy grasslands and highlight the important role of AED anomalies in it.

Mitigation Effect of Dense “Water Network” on Heavy PM2.5 Pollution: A Case Model of the Twain-Hu Basin, Central China

The influence of the underlying surface on the atmospheric environment over rivers and lakes is not fully understood. To improve our understanding, this study targeted the Twain-Hu Basin (THB) in central China, with a unique underlying surface comprising a dense "water network" over rivers and lakes. In this study, the Weather Research and Forecasting Model with Chemistry (WRF-Chem) was used to simulate the impact of this dense "water network" on a wintertime heavy PM2.5 pollution event in the THB. On this basis, the regulating effects of density and area of the lake groups, with centralized big lakes (CBLs) and discrete small lakes (DSLs), on PM2.5 concentrations over the underlying surface of the dense "water network" in the THB were clarified, and the relative contributions of thermal factors and water vapor factors in the atmospheric boundary layer to the variation of PM2.5 concentrations were evaluated. The results show that the underlying surface of dense "water networks" in the THB generally decreases the PM2.5 concentrations, but the influences of different lake-group types are not uniform in spatial distribution. The CBLs can reduce the PM2.5 concentrations over the lake and its surroundings by 4.90-17.68% during the day and night. The ability of DSLs in reducing PM2.5 pollution is relatively weak, with the reversed contribution between -5.63% and 1.56%. Thermal factors and water vapor-related factors are the key meteorological drivers affecting the variation of PM2.5 concentrations over the underlying surface of dense "water networks". The warming and humidification effects of such underlying surfaces contribute positively and negatively to the "purification" of air pollution, respectively. The relative contributions of thermal factors and water vapor-related factors are 52.48% and 43.91% for CBLs and 65.96% and 27.31% for DSLs, respectively. The "purification" effect of the underlying surface with a dense "water network" in the THB on regional air pollution highlights the importance of environmental protection of inland rivers and lakes in regional environmental governance. In further studies on the atmospheric environment, long-term studies are necessary, including fine measurements in terms of meteorology and the environment and more comprehensive simulations under different scenarios.

Analysis on temporal variation law of ozone pollution and meteorological driving factors in Pudong New Area of Shanghai

AbstractIn order to deeply understand the influence of meteorological drivers on ozone (O3) pollution during the high season and explore O3 pollution control options, we analyzed the temporal variation characteristics of O3 pollution based on the ambient air observation data of Pudong New Area, Shanghai from 2020 to 2021, studied the influence of meteorological drivers on O3 pollution, and explored seasonal emission reduction options based on the results of EKMA (Empirical Kinetics Modeling Approach) curves. The results show that the seasonal O3 concentration variation is spring > summer > autumn > winter; relative humidity, temperature and wind speed are the main meteorological drivers of O3 pollution; the cross‐regional transport of high O3 concentrations and precursors from the south and southwest has a strong influence on the local O3 concentration; Pudong New Area is in the “VOCs control zone” during the O3 pollution period in May. Therefore, it is important to control the production and emission of local VOCs and to strengthen the synergistic control of O3 and its precursors between regions for the control of spring O3 in Pudong New Area.

Constructing air temperature and relative humidity-based hourly thermal comfort dataset for a high-density city using machine learning

Global warming causes new challenges for urban citizens and metropolitan governments in adapting to the changing thermal environment. However, fine-scale spatiotemporal mapping of urban thermal environments has been inadequate. Therefore, this study takes a typical high-density city, Hong Kong, as an example and utilises a machine learning algorithm, the random forest (RF), to carry out 100 m resolution hourly thermal environment mapping, including air temperature (Ta), relative humidity (RH) and the net effective temperature (NET), for the summer season (May to September) of 2008–2018, considering meteorological drivers, topography and local-climate-zone-based landscape drivers. The validation results show that the developed dataset achieves satisfactory accuracy. The mean values of R2, root mean square error (RMSE) and mean absolute error (MAE) for Ta achieve 0.8723, 1.1160 °C and 0.8227 °C, respectively, while those for RH reach 0.7970, 5.3816% and 3.8641%. In addition, the thermal comfort index, NET, reveals that people in built-up areas feel hotter than measured by Ta during the night due to the urban heat island effect. We believe this newly developed thermal comfort dataset can provide novel, reliable and fine-grained data support for urban climate research areas such as urban heat islands, heat exposure, heat-related health risk assessment, and urban energy consumption estimation.

River flooding mechanisms and their changes in Europe revealed by explainable machine learning

Abstract. Climate change may systematically impact hydrometeorological processes and their interactions, resulting in changes in flooding mechanisms. Identifying such changes is important for flood forecasting and projection. Currently, there is a lack of observational evidence regarding trends in flooding mechanisms in Europe, which requires reliable methods to disentangle emerging patterns from the complex interactions between flood drivers. Recently, numerous studies have demonstrated the skill of machine learning (ML) for predictions in hydrology, e.g., for predicting river discharge based on its relationship with meteorological drivers. The relationship, if explained properly, may provide us with new insights into hydrological processes. Here, by using a novel explainable ML framework, combined with cluster analysis, we identify three primary patterns that drive 53 968 annual maximum discharge events in around a thousand European catchments. The patterns can be associated with three catchment-wide river flooding mechanisms: recent precipitation, antecedent precipitation (i.e., excessive soil moisture), and snowmelt. The results indicate that over half of the studied catchments are controlled by a combination of the above mechanisms, especially recent precipitation in combination with excessive soil moisture, which is the dominant mechanism in one-third of the catchments. Over the past 70 years, significant changes in the dominant flooding mechanisms have been detected within a number of European catchments. Generally, the number of snowmelt-induced floods has decreased significantly, whereas floods driven by recent precipitation have increased. The detected changes in flooding mechanisms are consistent with the expected climate change responses, and we highlight the risks associated with the resulting impact on flooding seasonality and magnitude. Overall, the study offers a new perspective on understanding changes in weather and climate extreme events by using explainable ML and demonstrates the prospect of future scientific discoveries supported by artificial intelligence.

Evaluation of wetland CH4in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations

Abstract. Wetlands are the largest natural source of methane. The ability to model the emissions of methane from natural wetlands accurately is critical to our understanding of the global methane budget and how it may change under future climate scenarios. The simulation of wetland methane emissions involves a complicated system of meteorological drivers coupled to hydrological and biogeochemical processes. The Joint UK Land Environment Simulator (JULES) is a process-based land surface model that underpins the UK Earth System Model (UKESM) and is capable of generating estimates of wetland methane emissions. In this study, we use GOSAT satellite observations of atmospheric methane along with the TOMCAT global 3-D chemistry transport model to evaluate the performance of JULES in reproducing the seasonal cycle of methane over a wide range of tropical wetlands. By using an ensemble of JULES simulations with differing input data and process configurations, we investigate the relative importance of the meteorological driving data, the vegetation, the temperature dependency of wetland methane production and the wetland extent. We find that JULES typically performs well in replicating the observed methane seasonal cycle. We calculate correlation coefficients to the observed seasonal cycle of between 0.58 and 0.88 for most regions; however, the seasonal cycle amplitude is typically underestimated (by between 1.8 and 19.5 ppb). This level of performance is comparable to that typically provided by state-of-the-art data-driven wetland CH4 emission inventories. The meteorological driving data are found to be the most significant factor in determining the ensemble performance, with temperature dependency and vegetation having moderate effects. We find that neither wetland extent configuration outperforms the other, but this does lead to poor performance in some regions. We focus in detail on three African wetland regions (Sudd, Southern Africa and Congo) where we find the performance of JULES to be poor and explore the reasons for this in detail. We find that neither wetland extent configuration used is sufficient in representing the wetland distribution in these regions (underestimating the wetland seasonal cycle amplitude by 11.1, 19.5 and 10.1 ppb respectively, with correlation coefficients of 0.23, 0.01 and 0.31). We employ the Catchment-based Macro-scale Floodplain (CaMa-Flood) model to explicitly represent river and floodplain water dynamics and find that these JULES-CaMa-Flood simulations are capable of providing a wetland extent that is more consistent with observations in this regions, highlighting this as an important area for future model development.

Impact of dynamic vegetation on near-surface meteorology using a newly developed WRF_NOAHMP_SUCROS coupled model

The study attempts to quantitatively understand the impact of dynamic vegetation on land-surface atmosphere interactions over spring wheat croplands in India. A new modeling tool capable of simulating these interactions was developed by incorporating the crop growth module of the Simple and Universal Crop growth Simulator (SUCROS) crop model into the Weather Research and Forecasting (WRF) mesoscale model. An earlier study had calibrated and evaluated the stand-alone SUCROS crop model with observed data for spring wheat collected from an experimental site in northwestern India. The crop growth module of the calibrated SUCROS model was implemented in the Noah-MP land module of WRF to build the coupled WRF_NOAHMP_SUCROS model. Numerical experiments were conducted with WRF_SUCROS that simulates the simultaneous evolution of meteorological drivers and crop Leaf Area Index (LAI) and the two-way interactions between these processes. These experiments were compared with WRF simulations driven by observed climatological mean LAI. These experiments only simulate the effects of changes in LAI on meteorology but not the other round. Results show that the coupled WRF_NOAHMP_SUCROS model is able to simulate the LAI better than the default dynamic vegetation module in WRF. It also produces realistic simulations of the near-surface meteorological parameters. The latent heat flux (LHF) varies directly with LAI, and sensible heat flux (SHF) varies inversely with LAI. As the crop grows, the energy transfer occurs more in latent heat flux than sensible heat flux due to increased evapotranspiration. Hence the growing crops result in near-surface cooling due to decreased Bowed Ratio. The mixing ratio is also increased due to increased latent heat flux. The uncoupled WRF model also shows similar patterns except in the juvenile crop stage where it overestimates the sensible heating and temperature but underestimates latent heat fluxes and mixing ratio.

Association between Global Monkeypox Cases and Meteorological Factors

The emergence of an outbreak of Monkeypox disease (MPXD) is caused by a contagious zoonotic Monkeypox virus (MPXV) that has spread globally. Yet, there is no study investigating the effect of climatic changes on MPXV transmission. Thus, studies on the changing epidemiology, evolving nature of the virus, and ecological niche are highly paramount. Determination of the role of potential meteorological drivers including temperature, precipitation, relative humidity, dew point, wind speed, and surface pressure is beneficial to understand the MPXD outbreak. This study examines the changes in MPXV cases over time while assessing the meteorological characteristics that could impact these disparities from the onset of the global outbreak. To conduct this data-based research, several well-accepted statistical techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Automatic forecasting time-series model (Prophet), and Autoregressive Integrated Moving Average with Explanatory Variables (ARIMAX) were applied to delineate the correlation of the meteorological factors on global daily Monkeypox cases. Data on MPXV cases including affected countries spanning from 6 May 2022, to 9 November 2022, from global databases and meteorological data were used to evaluate the developed models. According to the ARIMAX model, the results showed that temperature, relative humidity, and surface pressure have a positive impact [(51.56, 95% confidence interval (CI): -274.55 to 377.68), (17.32, 95% CI: -83.71 to 118.35) and (23.42, 95% CI: -9.90 to 56.75), respectively] on MPXV cases. In addition, dew/frost point, precipitation, and wind speed show a significant negative impact on MPXD cases. The Prophet model showed a significant correlation with rising MPXD cases, although the trend predicts peak values while the overall trend increases. This underscores the importance of immediate and appropriate preventive measures (timely preparedness and proactive control strategies) with utmost priority against MPXD including awareness-raising programs, the discovery, and formulation of effective vaccine candidate(s), prophylaxis and therapeutic regimes, and management strategies.

Response of a large deep-seated gravitational slope deformation to meteorological, seismic, and deglaciation drivers as measured by InSAR

We analyze the sensitivity of a large (area extent ∼3 km2), deep-seated gravitational slope deformation (Fels slide, Alaska Range) to three specific drivers: (i) liquid surface water input from ERA-5 reanalysis snow melt and rainfall; (ii) locally projected seismic activity of Alaskan earthquakes; and (iii) lowering of Fels Glacier at the slide toe estimated from topographic data. A surface displacement map-series is derived from 1991 to 2016 spaceborne multi-sensor InSAR data (ERS, RADARSAT-1/2, ALOS, TerraSAR-X) using adaptive demodulation to unwrap interferograms of variable spatial resolution and quality. On this series we use independent component analysis (ICA) to uncover five displacement patterns that map to independently moving domains of the slide and then correlate the corresponding temporal pattern intensities with the suspected drivers. We find significant sub-annual correlation between displacement pattern intensities and seasonal water input variations. The correlation can be optimized, for each ICA pattern, by choosing appropriate values of temporal smoothing and lag to create depth-propagated versions of the water input driver. Lag time results ranging from one to 3 weeks relate to shallower and deeper propagations of water input, driving the different deformation patterns. For two of the deformation patterns, seasonal sensitivity to water input was strongly amplified by the 2002 Mw7.9 Denali earthquake. Sensitivity of these patterns remained high for 4 years until abruptly dropping to below pre-earthquake values, which suggests a highly non-linear modulation by the seismic driver. Other deformation patterns show a steady intensity increase that appears linked to the deglaciation driver. Despite these observations, the inter-annual variations in ICA pattern intensities show no clear predictability by individual drivers or driver combinations. This suggests that the mechanical and hydraulic evolution of the slide, especially after damaging events such as earthquakes or heavy rainfall, is a crucial factor not adequately modeled in our approach. Despite this limitation, our analysis provides the first direct evidence that the Fels slide comprises several independently moving domains that respond differently to the suspected drivers as is suggestive of a complex slope deformation.

Forest–atmosphere exchange of reactive nitrogen in a remote region – Part II: Modeling annual budgets

Abstract. To monitor the effect of current nitrogen emissions and mitigation strategies, total (wet + dry) atmospheric nitrogen deposition to forests is commonly estimated using chemical transport models or canopy budget models in combination with throughfall measurements. Since flux measurements of reactive nitrogen (Nr) compounds are scarce, dry deposition process descriptions as well as the calculated flux estimates and annual budgets are subject to considerable uncertainties. In this study, we compared four different approaches to quantify annual dry deposition budgets of total reactive nitrogen (ΣNr) at a mixed forest site situated in the Bavarian Forest National Park, Germany. Dry deposition budgets were quantified based on (I) 2.5 years of eddy covariance flux measurements with the Total Reactive Atmospheric Nitrogen Converter (TRANC); (II) an in situ application of the bidirectional inferential flux model DEPAC (Deposition of Acidifying Compounds), here called DEPAC-1D; (III) a simulation with the chemical transport model LOTOS-EUROS (Long-Term Ozone Simulation – European Operational Smog) v2.0, using DEPAC as dry deposition module; and (IV) a canopy budget technique (CBT). Averaged annual ΣNr dry deposition estimates determined from TRANC measurements were 4.7 ± 0.2 and 4.3 ± 0.4 kg N ha−1 a−1, depending on the gap-filling approach. DEPAC-1D-modeled dry deposition, using concentrations and meteorological drivers measured at the site, was 5.8 ± 0.1 kg N ha−1 a−1. In comparison to TRANC fluxes, DEPAC-1D estimates were systematically higher during summer and in close agreement in winter. Modeled ΣNr deposition velocities (vd) of DEPAC-1D were found to increase with lower temperatures and higher relative humidity and in the presence of wet leaf surfaces, particularly from May to September. This observation was contrary to TRANC-observed fluxes. LOTOS-EUROS-modeled annual dry deposition was 6.5 ± 0.3 kg N ha−1 a−1 for the site-specific weighting of land-use classes within the site's grid cell. LOTOS-EUROS showed substantial discrepancies to measured ΣNr deposition during spring and autumn, which was related to an overestimation of ammonia (NH3) concentrations by a factor of 2 to 3 compared to measured values as a consequence of a mismatch between gridded input NH3 emissions and the site's actual (rather low) pollution climate. According to LOTOS-EUROS predictions, ammonia contributed most to modeled input ΣNr concentrations, whereas measurements showed NOx as the prevailing compound in ΣNr concentrations. Annual deposition estimates from measurements and modeling were in the range of minimum and maximum estimates determined from CBT being at 3.8 ± 0.5 and 6.7 ± 0.3 kg N ha−1 a−1, respectively. By adding locally measured wet-only deposition, we estimated an annual total nitrogen deposition input between 11.5 and 14.8 kg N ha−1 a−1, which is within the critical load ranges proposed for deciduous and coniferous forests.

Extreme events in the European renewable power system: Validation of a modeling framework to estimate renewable electricity production and demand from meteorological data

With the need to reduce greenhouse gas emissions, the coming decades will see a transition of Europe's power system, currently mainly based on fossil fuels towards a higher share of renewable sources. Increasing effects of fluctuations in electricity production and demand as a result of meteorological variability might cause compound events with unforeseen impacts. We constructed and validated a modeling framework to examine such extreme impact events on the European power system. This framework includes six modules: i) a reservoir hydropower inflow and ii) dispatch module; iii) a run-of-river hydropower production module; iv) a wind energy production module; v) a photovoltaic solar energy production model; and vi) an electricity demand module. Based on ERA5 reanalysis input data and present-day capacity distributions, we computed electricity production and demand for a set of European countries in the period 2015–2021 and compared results to observed data. The model captures the variability and extremes of wind, photovoltaic and run-of-river production well, with correlations between modelled and observed data for most countries of more than 0.87, 0.68 and 0.65 respectively. The hydropower dispatch module also functions well, with correlations up to 0.82, but struggles to capture reservoir inflows and operating procedures of some countries. A case study into the meteorological drivers of extreme events in Sweden and Spain showed that the meteorological conditions during extreme events selected by the model and extracted from observational data are similar, giving confidence in the application of the modeling framework for (future changes in) extreme event analysis.

Meteorological Drivers of Atmospheric Mercury Seasonality in the Temperate Northern Hemisphere

AbstractMercury (Hg) is a neurotoxic pollutant that can be transported globally by atmospheric circulation and poses risks to wildlife and humans. Strong atmospheric Hg seasonality has been observed in the temperate Northern Hemisphere and explained by several hypothetical mechanisms. Here, we found that meteorological conditions were important drivers of the seasonality across diverse temperate regions particularly Asia through various indirect effects. Meteorological conditions can explain approximately 47%, 43%, and 67% of the seasonal amplitudes at the North American (NA) remote sites, European remote sites, and Asian monsoon sites, respectively. Surface air temperature, solar radiation, and surface wind collectively contributed significantly to the seasonality at the NA and European sites through vegetation resistance and oceanic evasion, while monsoon wind and precipitation promoted the summertime removal of Hg at the Asian sites through atmospheric transport and wet deposition, respectively. The findings predicted further changes in atmospheric Hg seasonality under future global warming.

Changes in aerosol loading before, during and after the COVID-19 pandemic outbreak in China: Effects of anthropogenic and natural aerosol

Anthropogenic emissions reduced sharply in the short-term during the coronavirus disease pandemic (COVID-19). As COVID-19 is still ongoing, changes in atmospheric aerosol loading over China and the factors of their variations remain unclear. In this study, we used multi-source satellite observations and reanalysis datasets to synergistically analyze the spring (February–May) evolution of aerosol optical depth (AOD) for multiple aerosol types over Eastern China (EC) before, during and after the COVID-19 lockdown period. Regional meteorological effects and the radiative response were also quantitatively assessed. Compared to the same period before COVID-19 (i.e., in 2019), a total decrease of −14.6 % in tropospheric TROPOMI nitrogen dioxide (NO2) and a decrease of −6.8 % in MODIS AOD were observed over EC during the lockdown period (i.e., in 2020). After the lockdown period (i.e., in 2021), anthropogenic emissions returned to previous levels and there was a slight increase (+2.3 %) in AOD over EC. Moreover, changes in aerosol loading have spatial differences. AOD decreased significantly in the North China Plain (−14.0 %, NCP) and Yangtze River Delta (−9.4 %) regions, where anthropogenic aerosol dominated the aerosol loading. Impacted by strong wildfires in Southeast Asia during the lockdown period, carbonaceous AOD increased by +9.1 % in South China, which partially offset the emission reductions. Extreme dust storms swept through the northern region in the period after COVID-19, with an increase of +23.5 % in NCP and + 42.9 % in Northeast China (NEC) for dust AOD. However, unfavorable meteorological conditions overwhelmed the benefits of emission reductions, resulting in a +20.1 % increase in AOD in NEC during the lockdown period. Furthermore, the downward shortwave radiative flux showed a positive anomaly due to the reduced aerosol loading in the atmosphere during the lockdown period. This study highlights that we can benefit from short-term controls for the improvement of air pollution, but we also need to seriously considered the cross-regional transport of natural aerosol and meteorological drivers.

Air quality monitoring based on chemical and meteorological drivers: Application of a novel data filtering-based hybridized deep learning model

Particulate matter (PM) or particle pollution include the tiny particles of dust and fly ash particles are expelled from coal-burning power plants. Coal combustion is an extremely prevalent source of air pollution, and resulting PM has substantial impacts on human health, especially in industrial zones. This paper aims to design a novel hybrid deep learning framework based on long short-term memory (LSTM) integrated with a two-stage data filtering technique to accurately predict the air quality indices (i.e., PM2.5 and PM10) in a chosen study region ‘Miles Airport, Queensland,’ that meets the needs of the coal seam gas industry in Australia. The data used to construct the novel hybrid two-stage data filtering technique based on LSTM comprising of six meteorological parameters (i.e., wind direction, wind speed, air temperature, relative humidity, solar radiation, and rainfall) and two environmental factors (i.e., ozone and total suspended particulate). In the first stage, two robust feature selection methods, namely, extreme gradient boosting (XGBoost) and the classification and regression tree (CART) approach, were adopted to explore the most significant predictors. Then in the second stage, the best subset regression (BSR) technique is utilized to determine the best subsets input combinations i.e., C1, C2, and C3 based on several particular metrics. The three BSR-based input combinations were employed in the LSTM model to estimate the PM2.5 and PM10. Furthermore, to validate the main hybrid framework, two advanced machine learning (ML) methods (i.e., LightGBM and ridge kernel regression (KRR)) and two traditional ML methods (i.e., Adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron neural network (MLP)) were hybridized with the multi-level data filtering strategy by examining the optimal input combinations. Several statistical metrics, graphical tools, and diagnostic analyses evaluated the hybrid models. The outcomes of the PM2.5 simulation based on 2375 data samples showed that the LSTM-C3 containing all the selected predictors yielded the most promising accuracy, followed by the LightGBM-C3 and MLP-C3 models. On the other hand, the simulation of PM10 distribution demonstrated that the LSTM-C3 was superior to other models, followed by the KRR-C3 and LightGBM-C3 models.