Spatiotemporal dynamics and influencing factors of human brucellosis in Mainland China from 2005-2021.

BACKGROUND: Brucellosis poses a significant public health concern. This study explores the spatial and temporal dynamic evolution of human brucellosis in China and analyses the spatial heterogeneity of the influencing factors related to the incidence of human brucellosis at the provincial level. METHODS: The Join-point model, centre of gravity migration model and spatial autocorrelation analysis were employed to evaluate potential changes in the spatial and temporal distribution of human brucellosis in mainland China from 2005 to 2021. Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multi-scale Geographically Weighted Regression (MGWR) models were constructed to analyze the spatial and temporal correlation between the incidence rate of human brucellosis and meteorological and social factors. RESULTS: From 2005 to 2021, human brucellosis in China showed a consistent upward trend. The incidence rate rose more rapidly in South, Central, and Southwest China, leading to a shift in the center of gravity from the North to the Southwest, as illustrated in the migration trajectory diagram. Strong spatial aggregation was observed. The MGWR model outperformed others. Spatio-temporal plots indicated that lower mean annual temperatures and increased beef, mutton, and milk production significantly correlated with higher brucellosis incidence. Cities like Guangxi and Guangdong were more affected by low temperatures, while Xinjiang and Tibet were influenced more by beef and milk production. Inner Mongolia and Heilongjiang were more affected by mutton production. Importantly, an increase in regional GDP and health expenditure exerted a notable protective effect against human brucellosis incidence. CONCLUSIONS: Human brucellosis remains a pervasive challenge. Meteorological and social factors significantly influence its incidence in a spatiotemporally specific manner. Tailored prevention strategies should be region-specific, providing valuable insights for effective brucellosis control measures.

Relaxation of Spring Festival Firework Regulations Leads to a Deterioration in Air Quality.

The relaxation of restrictions on Chinese Spring Festival (SF) firework displays in certain regions has raised concerns due to intensive emissions exacerbating air quality deterioration. To evaluate the impacts of fireworks on air quality, a comparative investigation was conducted in a city between 2022 (restricted fireworks) and 2023 SF (unrestricted), utilizing high time-resolution field observations of particle chemical components and air quality model simulations. We observed two severe PM2.5 pollution episodes primarily triggered by firework emissions and exacerbated by static meteorology (contributing approximately 30%) during 2023 SF, contrasting with its absence in 2022. During firework displays, freshly emitted particles containing more primary inorganics (such as chloride and metals like Al, Mg, and Ba), elemental carbon, and organic compounds (including polycyclic aromatic hydrocarbons) were predominant; subsequently, aged particles with more secondary components became prevalent and continued to worsen air quality. The primary emissions from fireworks constituted 54% of the observed high PM2.5 during the displays, contributing a peak hourly PM2.5 concentration of 188 µg/m3 and representing over 70% of the ambient PM2.5. This study underscores that caution should be exercised when igniting substantial fireworks under stable meteorological conditions, considering both the primary and potential secondary effects.

Impact of air quality model settings for the evaluation of emission reduction strategies to curb air pollution.

For air quality management, while numerical tools are mainly evaluated to assess their performances on absolute concentrations, this study assesses the impact of their settings on the robustness of model responses to emission reduction strategies for the main criteria pollutants. The effect of the spatial resolution and chemistry schemes is investigated. We show that whereas the spatial resolution is not a crucial setting (except for NO2), the chemistry scheme has more impact, particularly when assessing hourly values of the absolute potential of concentrations. The analysis of model responses under the various configurations triggered an analysis of the impact of using online models, like WRF-chem or WRF-CHIMERE, which accounts for the impact of aerosol concentrations on meteorology. This study informs the air quality modeling community on what extent some model settings can affect the expected model responses to emission changes. We suggest to not activate online effects when analyzing the effect of an emission reduction strategy to avoid any confusion in the interpretation of results even if an online simulation should represent better the reality.

Analyzing the correlation between quinolone-resistant Escherichia coli resistance rates and climate factors: A comprehensive analysis across 31 Chinese provinces.

BACKGROUND: The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue. METHODS: We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis. RESULT: Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P < 0.001). Climate types influenced these rates, with significant variability observed across different climates (P < 0.001). Our predictive model for resistance rates, integrating climate and healthcare factors, explained 64.1% of the variance (adjusted R-squared = 0.641). For detection rates, the model accounted for 19.2% of the variance, highlighting the impact of environmental and healthcare influences. CONCLUSION: The study found higher resistance rates in warmer, monsoon climates and areas with more public health facilities, but lower rates in cooler, mountainous, or continental climates with more rainfall. This highlights the strong impact of climate on antibiotic resistance. Meanwhile, the predictive model effectively forecasts these resistance rates using China's diverse climate data. This is crucial for public health strategies and helps policymakers and healthcare practitioners tailor their approaches to antibiotic resistance based on local environmental conditions. These insights emphasize the importance of considering regional climates in managing antibiotic resistance.

Continental-scale evaluation of downy birch pollen production: Estimating the impacts of global change.

The high prevalence of hay fever in Europe has raised concerns about the implications of climate change-induced higher temperatures on pollen production. Our study focuses on downy birch pollen production across Europe by analyzing 456 catkins during 2019-2021 in 37 International Phenological Gardens (IPG) spanning a large geographic gradient. As IPGs rely on genetically identical plants, we were able to reduce the effects of genetic variability. We studied the potential association with masting behavior and three model specifications based on mean and quantile regression to assess the impact of meteorology (e.g., temperature and precipitation) and atmospheric gases (e.g., ozone (O3) and carbon-dioxide (CO2)) on pollen and catkin production, while controlling for tree age approximated by stem circumference. The results revealed a substantial geographic variability in mean pollen production, ranging from 1.9 to 2.5 million pollen grains per catkin. Regression analyses indicated that elevated average temperatures of the previous summer corresponded to increased pollen production, while higher O3 levels led to a reduction. Additionally, catkins number was positively influenced by preceding summer's temperature and precipitation but negatively by O3 levels. The investigation of quantile effects revealed that the impacts of mean temperature and O3 levels from the previous summer varied throughout the conditional response distribution. We found that temperature predominantly affected trees characterized by a high pollen production. We therefore suggest that birches modulate their physiological processes to optimize pollen production under varying temperature regimes. In turn, O3 levels negatively affected trees with pollen production levels exceeding the conditional median. We conclude that future temperature increase might exacerbate pollen production while other factors may modify (decrease in the case of O3 and amplify for precipitation) this effect. Our comprehensive study sheds light on potential impacts of climate change on downy birch pollen production, which is crucial for birch reproduction and human health.

Pan-African evolution of within- and between-country COVID-19 dynamics.

The coronavirus disease 2019 (COVID-19) pandemic is heterogeneous throughout Africa and threatening millions of lives. Surveillance and short-term modeling forecasts are critical to provide timely information for decisions on control strategies. We created a strategy that helps predict the country-level case occurrences based on cases within or external to a country throughout the entire African continent, parameterized by socioeconomic and geoeconomic variations and the lagged effects of social policy and meteorological history. We observed the effect of the Human Development Index, containment policies, testing capacity, specific humidity, temperature, and landlocked status of countries on the local within-country and external between-country transmission. One-week forecasts of case numbers from the model were driven by the quality of the reported data. Seeking equitable behavioral and social interventions, balanced with coordinated country-specific strategies in infection suppression, should be a continental priority to control the COVID-19 pandemic in Africa.

Weather, weekday, and vacation effects on webcam recorded daily visitor numbers in the alpine winter season.

Winter tourism is an important economic factor in the European Alps, which could be exposed to severely changing meteorological conditions due to climate change in the future. The extent to which meteorology influences winter tourism figures has so far been analyzed mainly based on monthly or seasonal data and in relation to skier numbers. Therefore, we record for the first time daily visitor numbers at five Bavarian winter tourism destinations based on 1518 webcam images using object detection and link them to meteorological and time-related variables. Our results show that parameters such as temperature, cloud cover or sunshine duration, precipitation, snow depth, wind speed, and relative humidity play a role especially at locations that include other forms of winter tourism in addition to skiing. In the ski resorts studied, on the other hand, skiing is mostly independent of current weather conditions, which can be attributed mainly to artificial snowmaking. Moreover, at the webcam sites studied, weekends and vacation periods had an equal or even stronger influence on daily visitor numbers than the current weather conditions. The extent to which weather impacts the (future) visitor numbers of a winter tourism destination must therefore be investigated individually and with the inclusion of non-meteorological variables influencing human behavior.

Validation of Citizen Science Meteorological Data: Can They Be Considered a Valid Help in Weather Understanding and Community Engagement?

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect meteorological measurements. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. This paper presents the analysis and the results of the validation of such observations against the official Italian civil protection in situ weather network, demonstrating the effectiveness of citizen science in generating valuable environmental data. The work discusses the methodology employed, including data collection and statistical analysis techniques, i.e., time-series analysis, spatial and temporal interpolation, and correlation analysis. The overall analysis highlights the high quality and reliability of citizen-generated data as well as the strengths of the MeteoTracker platform. Furthermore, our findings underscore the potential of citizen science to augment traditional monitoring efforts, inform decision-making processes in environmental research and management, and improve the social awareness about environmental and climate issues.

ZPD Retrieval Performances of the First Operational Ship-Based Network of GNSS Receivers over the North-West Mediterranean Sea.

This work presents the design and implementation of an operational infrastructure for the monitoring of atmospheric parameters at sea through GNSS meteorology sensors installed on liners operating in the north-west Mediterranean Sea. A measurement system, capable of operationally and continuously providing the values of surface parameters, is implemented together with software procedures based on a float-PPP approach for estimating zenith path delay (ZPD) values. The values continuously registered over a three year period (2020-2022) from this infrastructure are compared with the data from a numerical meteorological reanalysis model (MERRA-2). The results clearly prove the ability of the system to estimate the ZPD from ship-based GNSS-meteo equipment, with the accuracy evaluated in terms of correlation and root mean square error reaching values between 0.94 and 0.65 and between 18.4 and 42.9 mm, these extreme values being from the best and worst performing installations, respectively. This offers a new perspective on the operational exploitation of GNSS signals over sea areas in climate and operational meteorological applications.

Comprehensive analysis of air pollution and the influence of meteorological factors: a case study of adiyaman province.

Adiyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM10 and SO2, indicating moderate positive correlations under pressure conditions (r = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed (r = -0.35, -0.50), and temperature also exhibits a negative correlation (r = -0.39, -0.54). During atmospheric conditions with high pressure, PM10 and SO2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake.

syN-BEATS for robust pollutant forecasting in data-limited context.

This research introduces syN-BEATS, a novel ensemble deep learning model tailored for effective pollutant forecasting under conditions of limited data availability. Based on the N-BEATS architecture, syN-BEATS integrates various configurations with differing numbers of stacks and blocks, effectively combining weak and strong learning approaches. Our experiments show that syN-BEATS outperforms standard models, especially when using Bayesian optimization to fine-tune ensemble weights. The model consistently achieves low relative root mean square errors, proving its capacity for precise pollutant forecasting despite data constraints. A key aspect of this study is the use of data from only one meteorological and one air quality monitoring station per region, simulating environments with restricted monitoring capabilities. By applying this approach in regions with diverse climates and air quality levels, we thoroughly assess the model's flexibility and resilience under different environmental conditions. The results highlight syN-BEATS' ability to support the development of effective health alert systems that can detect specific airborne pollutants, even in areas with limited monitoring infrastructure. This advancement is crucial for enhancing environmental monitoring and public health management in under-resourced areas.

Urban ozone variability using automated machine learning: inference from different feature importance schemes.

Tropospheric ozone is an air pollutant at the ground level and a greenhouse gas which significantly contributes to the global warming. Strong anthropogenic emissions in and around urban environments enhance surface ozone pollution impacting the human health and vegetation adversely. However, observations are often scarce and the factors driving ozone variability remain uncertain in the developing regions of the world. In this regard, here, we conducted machine learning (ML) simulations of ozone variability and comprehensively examined the governing factors over a major urban environment (Ahmedabad) in western India. Ozone precursors (NO2, NO, CO, C5H8 and CH2O) from the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis and meteorological parameters from the ERA5 (European Centre for Medium-Range Weather Forecast's (ECMWF) fifth-generation reanalysis) were included as features in the ML models. Automated ML (AutoML) fitted the deep learning model optimally and simulated the daily ozone with root mean square error (RMSE) of ~2 ppbv reproducing 84-88% of variability. The model performance achieved here is comparable to widely used ML models (RF-Random Forest and XGBoost-eXtreme Gradient Boosting). Explainability of the models is discussed through different schemes of feature importance, including SAGE (Shapley Additive Global importancE) and permutation importance. The leading features are found to be different from different feature importance schemes. We show that urban ozone could be simulated well (RMSE = 2.5 ppbv and R2 = 0.78) by considering first four leading features, from different schemes, which are consistent with ozone photochemistry. Our study underscores the need to conduct science-informed analysis of feature importance from multiple schemes to infer the roles of input variables in ozone variability. AutoML-based studies, exploiting potentials of long-term observations, can strongly complement the conventional chemistry-transport modelling and can also help in accurate simulation and forecast of urban ozone.

The chymistry of rainbows, winds, lightning, heat and cold in Paracelsus.

Meteorology is not one of the most discussed topics in Paracelsus studies, although it is closely linked to both Paracelsus' medicine and cosmology. Furthermore, it appears to be at the very core of Paracelsus' famous matter theory of three chymical principles, mercury, sulphur and salt, known as the tria prima. By discussing prominent examples of Paracelsus' explanations on how the tria prima operate within the stars, this article shows how the Swiss physician conceived meteorology within his own body of knowledge, obviously constructed in opposition to the Aristotelian-scholastic tradition, how he based it on a peculiar interpretation of the Biblical creation story, and made it the proper laboratory of his chymical matter theory, applying it first systematically to the field of natural philosophy, especially to celestial phenomena, even before using it for his medical theory in his later writings.

Of comets and cosmology in Antonino Saliba's Nuova Figura di tutte le cose of 1582.

Antonino Saliba, a sixteenth century cartographer hailing from the Maltese island of Gozo, published a map in 1582 espousing his cosmology. Its popularity at the time is attested via the multiple editions and copies that were produced in Europe. Numerous sky phenomena, amongst them comets, are portrayed in the map. This study presents a detailed analysis of Saliba's treatment of these phenomena, following the first comprehensive translation of the map's text to English. It elucidates the sources that Saliba used, clarifying and shedding further light on the views he held. Where possible, the comets mentioned by Saliba are identified and explained. Besides showing how Saliba wholly conformed to the Aristotelian and Ptolemaic representation of the world, in which respect he was quite orthodox, it is also shown for the first time that his work is significantly derived from previous and contemporary sources.

Significant Reductions in Secondary Aerosols after the Three-Year Action Plan in Beijing Summer.

Air quality in China has continuously improved during the Three-Year Action Plan (2018-2020); however, the changes in aerosol composition, properties, and sources in Beijing summer remain poorly understood. Here, we conducted real-time measurements of aerosol composition in five summers from 2018 to 2022 along with WRF-Community Multiscale Air Quality simulations to characterize the changes in aerosol chemistry and the roles of meteorology and emission reductions. Largely different from winter, secondary inorganic aerosol and photochemical-related secondary organic aerosol (SOA) showed significant decreases by 55-67% in summer, and the most decreases occurred in 2021. Comparatively, the decreases in the primary aerosol species and gaseous precursors were comparably small. While decreased atmospheric oxidation capacity as indicated by ozone changes played an important role in changing SOA composition, the large decrease in aerosol liquid water and small increase in particle acidity were critical for nitrate changes by decreasing gas-particle partitioning substantially (∼28%). Analysis of meteorological influences demonstrated clear and similar transitions in aerosol composition and formation mechanisms at a relative humidity of 50-60% in five summers. Model simulations revealed that emission controls played the decisive role in reducing sulfate, primary OA, and anthropogenic SOA during the Three-Year Action Plan, while meteorology affected more nitrate and biogenic SOA.

Atmospheric Transport of Polycyclic Aromatic Hydrocarbons into Three Alpine Valleys: Influence of Local-Scale Wind Patterns and Chemical Partitioning.

Current understanding of atmospheric transport of polycyclic aromatic hydrocarbons (PAHs) is limited in alpine areas due to complex meteorology and topography. To better understand atmospheric transport in these areas, we measured 16 PAHs in lichens, biomonitors of atmospheric PAHs, along three transects extending from a highway into otherwise remote alpine valleys. While the valleys neighbored one another and were morphologically similar, they differed in their orientation relative to regional winds. In the valley characterized by regional winds oriented up-valley, PAH concentrations in lichens remained consistent across the transect. In the other two valleys, where regional winds were oriented down or across the valley, 3-6 ring PAHs declined rapidly with increasing distance from the highway, and PAH concentrations in the lichens declined more rapidly for higher molecular weight PAHs than lower molecular weight PAHs. We hypothesize that this trend was driven by differences in gas-particle partitioning and vegetative scavenging between PAH congeners. These results illustrate the importance of both physical transport and chemical partitioning in alpine areas where small differences in topography can lead to significant differences in chemical transport.

Drivers of Increasing Ozone during the Two Phases of Clean Air Actions in China 2013-2020.

In response to the severe air pollution issue, the Chinese government implemented two phases (Phase I, 2013-2017; Phase II, 2018-2020) of clean air actions since 2013, resulting in a significant decline in fine particles (PM2.5) during 2013-2020, while the warm-season (April-September) mean maximum daily 8 h average ozone (MDA8 O3) increased by 2.6 µg m-3 yr-1 in China during the same period. Here, we derived the drivers behind the rising O3 concentrations during the two phases of clean air actions by using a bottom-up emission inventory, a regional chemical transport model, and a multiple linear regression model. We found that both meteorological variations (3.6 µg m-3) and anthropogenic emissions (6.7 µg m-3) contributed to the growth of MDA8 O3 from 2013 to 2020, with the changes in anthropogenic emissions playing a more important role. The anthropogenic contributions to the O3 rise during 2017-2020 (1.2 µg m-3) were much lower than that in 2013-2017 (5.2 µg m-3). The lack of volatile organic compound (VOC) control and the decline in nitrogen oxides (NOx) emissions were responsible for the O3 increase in 2013-2017 due to VOC-limited regimes in most urban areas, while the synergistic control of VOC and NOx in Phase II initially worked to mitigate O3 pollution during 2018-2020, although its effectiveness was offset by the penalty of PM2.5 decline. Future mitigation efforts should pay more attention to the simultaneous control of VOC and NOx to improve O3 air quality.

Why did air quality experience little improvement during the COVID-19 lockdown in megacities, northeast China?

To inhibit the COVID-19 (Coronavirus disease 2019) outbreak, unprecedented nationwide lockdowns were implemented in China in early 2020, resulting in a marked reduction of anthropogenic emissions. However, reasons for the insignificant improvement in air quality in megacities of northeast China, including Shenyang, Changchun, Jilin, Harbin, and Daqing, were scarcely reported. We assessed the influences of meteorological conditions and changes in emissions on air quality in the five megacities during the COVID-19 lockdown (February 2020) using the WRF-CMAQ model. Modeling results indicated that meteorology contributed a 14.7% increment in Air Quality Index (AQI) averaged over the five megacities, thus, the local unfavorable meteorology was one of the causes to yield little improved air quality. In terms of emission changes, the increase in residential emissions (+15%) accompanied by declining industry emissions (-15%) and transportation (-90%) emissions resulted in a slight AQI decrease of 3.1%, demonstrating the decrease in emissions associated with the lockdown were largely offset by the increment in residential emissions. Also, residential emissions contributed 42.3% to PM2.5 concentration on average based on the Integrated Source Apportionment tool. These results demonstrated the key role residential emissions played in determining air quality. The findings of this study provide a scenario that helps make appropriate emission mitigation measures for improving air quality in this part of China.

Drought and ozone air quality in California: Identifying susceptible regions in the preparedness of future drought.

California experienced extreme and prolonged drought conditions during the early 2010s. To date, little is known regarding the influence of drought on air quality. Our study quantified site-specific associations between drought (defined by the Standardized Precipitation-Evapotranspiration Index; SPEI) and daily maximum 8-h ozone (O3) concentrations for California, USA, and then pooled these associations for the years 2009-2015. Overall, ambient O3 concentration was higher during droughts by 1.18 ppb (95% confidence interval (CI) = 1.00-1.36). The sensitivity of O3 to drought was greater during the warm season than during the cool season (1.73 ppb versus 0.79 ppb higher O3 during droughts) with substantial regional variation. In a pooled analysis with meteorological parameters as potential effect modifiers, the spatial heterogeneity of drought-O3 associations was explained strongly by average relative humidity for each season (71.9% (warm season) and 73.4% (cool season) of the drought-O3 associations explained), followed by the drought-related changes in relative humidity (47.6% (warm season)) and temperature (53.6% (cool season)). The pooled regression further identified regions susceptible for drought-related O3 increases as those with relatively low average relative humidity (10-25th percentiles or 44.3-47.3%) and larger drought-related decrease in relative humidity and increase in temperature. As the drought events are projected to occur with increased frequency and intensity in the era of climate change, the excess health burdens from O3 exposures attributed to the projected drought events need to be taken into account when allocating air quality and health resources. The impacts of O3 on health during droughts would confound the health burdens from the drought itself.

Impact of COVID-19 restrictions on hourly levels of PM10, PM2.5 and black carbon at an industrial suburban site in northern Spain.

Due to the COVID-19 pandemic, lockdown restrictions were established around the world. Many studies have assessed whether these restrictions affected atmospheric pollution. Comparison between them is difficult as the periods of time considered are generally not the same and thus, different conclusions may be reached. Besides, most of them consider mean daily pollutant concentration, despite differences being observed according to the time of day. In this study, the hourly levels of PM10, PM2.5 and black carbon (BC) in an industrial suburban area in the north of Spain were analysed from May 2019 to June 2020 and compared with those from the literature, using the same period in each case. In general, the highest concentrations were reached when the wind direction came from the southwest (where a steelworks, a coal-fired power plant and other industries are located) and during the night-time, both before and during the lockdown. The highest concentrations of PM10, PM2.5 and BC were observed from December to February (on average: 45, 17 and 1.3 µg m-3, respectively). The decrease/increase in those pollutants levels during the lockdown were found to be highly dependent on the period considered. Indeed, PM10 can be found to decrease by up to 39% or increase by 12%; PM2.5 can decrease by 21% or increase by up to 36%; and BC, although it generally decreases (by up to 42%), can increase by 7.4%.