国内外臭氧污染时空分布规律

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Nighttime ozone enhancement (NOE) can increase the oxidation capacity of the atmosphere by stimulating nitrate radical formation and subsequently facilitating the formation of secondary pollutants, thereby affecting air quality in the following days. Previous studies have demonstrated that when nocturnal ozone (O3) concentrations exceed 80 μg/m3, it leads to water loss and reduction of plant yields. In this study, the characteristics and mechanisms of NOE over Shandong Province as well as its 16 cities were analyzed based on observed hourly O3 concentrations from 2020 to 2022. The analysis results show that NOE predominantly occurred in the periods of 0:00-3:00 (41 %). The annual mean frequency of NOE events was ~64 days/year, approximately 4-7 days per month. The average concentration of nocturnal O3 peak (NOP) was ~72.6 μg/m3. Notably, high NOP was observed in the period from April to September with the maximum in June. Coastal cities experienced more NOE events. Typical NOE events characterized by high NOP concentrations in the coastal cities of QingDao, WeiHai and YanTai in June 2021 were selected for detailed analysis with a regional chemical transport model. The results showed that high levels of O3 in eastern coastal cities during NOE events primarily originate from horizontal transport over the sea, followed by vertical transport. During the daytime, O3 and its precursors are transported to the Yellow Sea by westerly winds, leading to the accumulation of O3 near the sea and coastline. Consequently, under the influence of prevailing winds, the movement of O3 pollution belts from the sea to land causes rapid increases in near-surface O3 levels. Meanwhile, vertical transport can also contribute to NOE in coastal areas. The high-level O3 in the upper atmosphere generally originates from long-distance transport and turbulent transport of O3 produced near the ground during the daytime. At night, the absence of chemicals that consume O3 in the upper air and descending air flow carries O3 to the near-surface. The impacts of other O3-depletion processes (such as dry deposition) on NOE are less pronounced than those of transport processes.

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Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

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In recent years, ozone (O 3 ) pollution in China has shown a worsening trend. Due to the vast territory of China, O 3 pollution is a widespread and complex problem. It is vital to understand the current spatiotemporal distribution of O 3 pollution in China. In this study, we collected hourly data on O 3 concentrations in 338 cities from January 1, 2016, to February 28, 2019, to analyze O 3 pollution in China from a spatiotemporal perspective. The spatial analysis showed that the O 3 concentrations exceeded the limit in seven geographical regions of China to some extent, with more serious pollution in North, East, and Central China. The O 3 concentrations in the eastern areas were usually higher than those in the western areas. The temporal analysis showed seasonal variations in O 3 concentration, with the highest O 3 concentration in the summer and the lowest in the winter. The weekend effect, which occurs in other countries (such as the USA), was found only in some cities in China. We also found that the highest O 3 concentration usually occurred in the afternoon and the lowest was in the early morning. The comprehensive analysis in this paper could improve our understanding of the severity of O 3 pollution in China.

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Accurate and fine‐scaled prediction of ozone concentrations across space and time, as well as the assessment of associated human risks, is crucial for protecting public health and promoting environmental conservation. This paper introduces NetGBM, an innovative machine‐learning model designed to comprehensively model ozone levels across China's diverse topography and analyze the spatiotemporal distribution of ozone and exposure. Our model focuses on daily, weekly, and monthly predictions, achieving commendable R2 ${\mathrm{R}}^{2}$ coefficients of 0.83, 0.77, and 0.79, respectively. By constructing a gridded map of ozone and incorporating both land use and meteorological features into each grid, we achieved ozone prediction at a high spatiotemporal resolution, outperforming previous research in terms of performance and scale, particularly in regions with limited monitoring stations. The results can be further improved when applied to regional research using meteorological and ozone data from regional stations. Additionally, our research revealed that temperature is the most significant factor affecting ozone concentrations across China. In health risk assessment, we retrieved a high‐resolution spatial distribution of ozone‐attributed mortality for 5‐COD and daily ozone inhalation distributions during our study period. We concluded that ozone‐attributed mortality is predominantly caused by stroke and IHD, accounting for more than 70% of the total deaths in 2021, with the highest mortality rates in developed urban areas such as the NCP and the YRD. Our experiment demonstrated the potential of NetGBM in robustly modeling ozone across China with high spatiotemporal resolution and its applicability in measuring associated health risks.

Tropospheric ozone affects human health, ecosystems, and climate change. Previous studies on Tropospheric Column Ozone (TCO) have primarily concentrated on specific regions or global geographic divisions. This has led to insufficient exploration of the spatiotemporal characteristics and influencing factors of TCO in global and rational subregions. In this study, TCO is calculated using the Modern Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data and corrected using satellite data. Cluster analysis is conducted to explore the temporal characteristics of TCO variations in different regions. The results show that the global TCO is basically distributed latitudinally, with higher TCO in the northern hemisphere, which is related to atmospheric circulation, radiation, stratospheric transport, and the distribution of ozone precursors. Between 1980 and Ma, 2020, the global average annual TCO showed an increasing trend at 0.09 DU yr-1 due to rising anthropogenic emissions of ozone precursors (NOx at 589547.86 t yr-1 and NMVOC at 1070818.24 t yr-1), increasing tropopause height (-0.10 hPa yr-1), and the enhanced ozone flux at the tropopause (0.22 ppbv m s-2 yr-1). Cluster analysis reveals different trends in TCO changes across regions. The ocean south of 60°S and parts of West Antarctica (Region 2), the region from 30°N to 60°N and the western oceanic region of 30°S (Region 3), and the region from the equator to 60°S and the region north of 60°N (Region 5) exhibit increasing trends (with rates of 0.08 DU yr-1, 0.07 DU yr-1, and 0.11 DU yr-1, respectively), linked to the enhanced ozone flux at the tropopause, the rising tropopause height and increasing ozone p precursors. Conversely, the decreasing TCO trends in the equatorial Pacific (Region 1) and East Antarctica (Region 4) (with rates of -0.01 DU yr-1 and -0.02 DU yr-1) may be related to increased cloudiness and weakened photochemical reactions.

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Air pollution is a serious environmental health concern for humans and other living organisms. This study analyzes the spatial and temporal characteristics of air pollutant concentrations, changes in the degree of pollution, and the wavelet coherence of the air quality index (AQI) with pollutants in various monitoring stations. The analysis is based on long-term time series data (January 2016 to December 2023) of air pollutants (PM2.5, PM10, and O3) from Korla, an oasis city in the northeastern part of the Tarim Basin, China. The concentrations of PM2.5, PM10, and O3 in Korla showed a cyclical trend from 2016 to 2023; PM10 concentrations exhibited all-season exceedance and PM2.5 exhibited exceedance only in spring. PM2.5 and PM10 showed a seasonal distribution of spring > winter > fall > summer; O3 concentrations showed a seasonal distribution of summer > spring > fall > winter. Strong positive wavelet coherence between PM and Air Quality Index (AQI) data series suggests that the AQI data series can effectively characterize fluctuating trends in PM concentrations. Moreover, PM10 levels IV and VI were maintained at approximately 10%, indicating that sand and dust have a substantial influence on air quality and pose potential threats to the health of urban inhabitants. Based on the results of this study, future efforts must strengthen relative countermeasures for sand prevention and control, select urban greening species with anti-pollution capabilities, rationally expand urban green spaces, and restrict regulations for reducing particulate matter emissions within city areas.

As a critical core node of the “Belt and Road” Initiative and a representative arid-zone urban agglomeration in Northwest China, the Urumqi-Changji-Shihezi (U-Chang-Shi) region faces severe air pollution, posing significant threats to ecological security and public health. Leveraging the 2000–2022 China High-Resolution Air Quality (CHAP) dataset and multi-source meteorological data, this study systematically investigates the spatiotemporal evolution of PM2.5, PM10, and ozone (O3) alongside their driving mechanisms. Results reveal distinct seasonal patterns: PM2.5 and PM10 concentrations peak in winter due to coal combustion emissions and unfavorable static meteorological conditions, while dropping below 30 µg/m3 in summer as photochemical reactions weaken. The Mann–Kendall (MK) trend test, combined with spatial-temporal analysis methods, elucidates the complex pollution dynamics. The U-Chang-Shi industrial belt acts as a pollution hotspot, with Dabancheng District exhibiting elevated PM10 levels attributed to pollutant transport and terrain effects. O3 pollution intensifies in spring and summer, surging post-2016 across regional cities, with Shihezi showing a 16.7% annual increase. Key drivers include unfavorable static meteorology and sparse vegetation for particulate pollutants, while precipitation (P) wet deposition enhances their removal. O3 production is modulated by potential evapotranspiration (PET) and wind speed (WIND), with high temperatures (T) accelerating photochemical reactions, although counteracted by particulate matter. Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) simulations indicate that Eurasian mid-latitude winter circulation and cross-border dust contribute to winter PM10 variability. Although the “coal-to-gas” project mitigated particulate pollution, its efficacy is constrained by Shihezi’s lagging industrial restructuring. This study provides critical insights for optimizing air pollution control strategies in ecologically vulnerable regions of Northwest China and arid-zone urban agglomerations under the Belt and Road Initiative, emphasizing the need for region-specific emission reduction measures and cross-border collaboration.

Accurate prediction of spatiotemporal ozone concentration is of great significance to effectively establish advanced early warning systems and regulate air pollution control. However, the comprehensive assessment of uncertainty and heterogeneity in spatiotemporal ozone prediction remains unknown. Here, we systematically analyze the hourly and daily spatiotemporal predictive performances using convolutional long short term memory (ConvLSTM) and deep convolutional generative adversarial network (DCGAN) models over the Beijing-Tianjin-Hebei region in China from 2013 to 2018. In extensive scenarios, our results show that the machine learning-based (ML-based) models achieve better spatiotemporal ozone concentration prediction performance with multiple meteorological conditions. A further comparison to the air pollution model-Nested Air Quality Prediction Modelling System (NAQPMS) and monitoring observations, the ConvLSTM model demonstrates the practical feasibility of identifying high ozone concentration distribution and capturing spatiotemporal ozone variation patterns at a high spatial resolution (here 15 km × 15 km).

This study aims to support the development of Spain's Ozone Mitigation Plan by evaluating the present-day spatial variation (2015-2019) and trends (2008-2019) for seven ground-level ozone (O3) metrics relevant for human/ecosystems exposure and regulatory purposes. Results indicate that the spatial variation of O3 depends on the part of the O3 distribution being analyzed. Metrics associated with moderate O3 concentrations depict an increasing O3 gradient between the northern and Mediterranean coasts due to climatic factors, while for metrics considering the upper end of the O3 distribution, this climatic gradient tends to attenuate in favor of hotspot regions pointing to relevant local/regional O3 formation. A classification of atmospheric regions in Spain is proposed based on their O3 pollution patterns, to identify priority areas (or O3 hotspots) where local/regional precursor abatement might significantly reduce O3 during pollution episodes. The trends assessment reveals a narrowing of the O3 distribution at the national level, with metrics influenced by lower concentrations tending to increase over time, and those reflecting the higher end of the O3 distribution tending to decrease. While most stations show no statistically significant variations, contrasting O3 trends are evident among the O3 hotspots. The Madrid area exhibits the majority of upward trends across all metrics, frequently with the highest increasing rates, implying increasing O3 associated with both chronic and episodic exposure. The Valencian Community area exhibits a mixed variation pattern, with moderate to high O3 metrics increasing and peak metrics decreasing, while O3 in areas downwind of Barcelona, the Guadalquivir Valley and Puertollano shows no variations. Sevilla is the only large Spanish city with generalized O3 decreasing trends. The different O3 trends among hotspots highlight the need for mitigation measures to be designed at a local/regional scale to be effective. This approach may offer valuable insights for other countries developing O3 mitigation plans.

The spatiotemporal characteristics of ozone pollution and increased human health risks in Central China were investigated using a long time series of ozone concentrations from 2014 to 2020. We found a gradual increase in ozone pollution, with the highest concentrations observed in the northeastern region. The spatial distribution of population density showed distinct patterns, with the northeastern and east-central regions coinciding with areas of high ozone concentrations. The study found an overall increasing trend in MDA8 ozone concentrations, with a regional average increase of 3.5 (μg m−3) per year, corresponding to a 4.4% annual increase. We observed a significant clustering of areas at a higher risk of premature mortality associated with long-term ozone exposure, particularly in the northeastern region. Estimated premature mortality due to ozone pollution in Central China between 2014 and 2020 shows an increasing trend from 2014 to 2019 and a decreasing trend in 2020 due to the occurrence of extreme ozone pollution and the subsequent recovery of ozone concentrations after the closures due to COVID-19. Premature mortality due to ozone exposure is affected by both ozone levels and the exposed population, with high correlation coefficients exceeding 0.95. The high total population (more than 220 million per year) and increasing ozone levels exacerbate the problem of premature mortality due to ozone pollution. This study improves our understanding of the impact of ozone pollution on human health and emphasizes the dynamic nature of ozone pollution and its impacts on human health over time. It underscores the need for further study and comprehensive action to mitigate these health risks.

Evaluating ozone levels at high resolutions and accuracy is crucial for understanding the spatiotemporal characteristics of ozone distribution and assessing ozone exposure levels in epidemiological studies. The national models with high spatiotemporal resolutions to predict ground ozone concentrations are limited in China so far. In this study, we aimed to develop a random forest model by combining ground ozone measurements from fixed stations, ozone simulations from the Community Multiscale Air Quality (CMAQ) modeling system, meteorological parameters, population density, road length, and elevation to predict ground maximum daily 8-h average (MDA8) ozone concentrations at a daily level and 1 km × 1 km spatial resolution. The model cross-validation R2 and root mean squared error (RMSE) were 0.80 and 20.93 μg/m3 at daily level in 2013-2019, respectively. CMAQ ozone simulations and near-surface temperature played vital roles in predicting ozone concentrations among all predictors. The population-weighted median concentrations of predicted MDA8 ozone were 89.34 μg/m3 in mainland China in 2013, and reached 100.96 μg/m3 in 2019. However, the long-term temporal variations among regions were heterogeneous. Central and Eastern China, as well as the Southeast Coastal Area, suffered higher ozone pollution and higher increased rates of ozone concentrations from 2013 to 2019. The seasonal pattern of ozone pollution varied spatially. The peak-season ozone pollution with the highest 6-month ozone concentrations occurred in different months among regions, with more than half domain in April-September. The predictions showed that not only the annual mean concentrations but also the percentages of grid-days with MDA8 ozone concentrations higher than 100/160 μg/m3 have been increasing in the past few years in China; meanwhile, majority areas in mainland China suffered peak-season ozone concentrations higher than the air quality guidelines launched by the World Health Organization in September 2021. The proposed model and ozone predictions with high spatiotemporal resolution and full coverage could provide health studies with flexible choices to evaluate ozone exposure levels at multiple spatiotemporal scales in the future.

Background: Although short-term ozone (O3) exposure has been associated with a series of adverse health outcomes, research on the health effects of chronic O3 exposure is still limited, especially in developing countries because of the lack of long-term exposure estimates. Objectives: The present study aimed to estimate the spatiotemporal distribution of monthly mean daily maximum 8-h average O3 concentrations in China from 2005 to 2019 at a 0.05° spatial resolution. Methods: We developed a machine learning model with a satellite-derived boundary-layer O3 column, O3 precursors, meteorological conditions, land-use information, and proxies of anthropogenic emissions as predictors. Results: The random, spatial, and temporal cross-validation R2 of our model were 0.87, 0.86, and 0.76, respectively. Model-predicted spatial distribution of ground-level O3 concentrations showed significant differences across seasons. The highest summer peak of O3 occurred in the North China Plain, whereas southern regions were the most polluted in winter. Most large urban centers showed elevated O3 levels, but their surrounding suburban areas may have even higher O3 concentrations owing to nitrogen oxides titration. The annual trend of O3 concentrations fluctuated over 2005–2013, but a significant nationwide increase was observed afterward. Discussion: The present model had enhanced performance in predicting ground-level O3 concentrations in China. This national data set of O3 concentrations would facilitate epidemiological studies to investigate the long-term health effect of O3 in China. Our results also highlight the importance of controlling O3 in China’s next round of the Air Pollution Prevention and Control Action Plan. https://doi.org/10.1289/EHP9406

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Variations in marine and terrestrial geographical environments can cause considerable differences in meteorological conditions, economic features, and population density (PD) levels between coastal and inland cities, which in turn can affect the urban air quality. In this study, a five-year (2016–2020) dataset encompassing air monitoring (from the China National Environmental Monitoring Centre), socioeconomic statistical (from the Shandong Province Bureau of Statistics) and meteorological data (from the U.S. National Centers for Environmental Information, National Oceanic and Atmospheric Administration) was employed to investigate the spatiotemporal distribution characteristics and underlying drivers of urban ozone (O3) in Shandong Province, a region with both land and sea environments in eastern China. The main research methods included the multiscale geographically weighted regression (MGWR) model and wavelet analysis. From 2016 to 2019, the O3 concentration increased year by year in most cities, but in 2020, the O3 concentration in all cities decreased. O3 concentration exhibited obvious regional differences, with higher levels in inland areas and lower levels in eastern coastal areas. The MGWR analysis results indicated the relationship between PD, urbanization rate (UR), and O3 was greater in coastal cities than that in the inland cities. Furthermore, the wavelet coherence (WTC) analysis results indicated that the daily maximum temperature was the most important factor influencing the O3 concentration. Compared with NO, NO2, and NOx (NOx ≡ NO + NO2), the ratio of NO2/NO was more coherent with O3. In addition, the temperature, the wind speed, nitrogen oxides, and fine particulate matter (PM2.5) exerted a greater impact on O3 in coastal cities than that in inland cities. In summary, the effects of the various abovementioned factors on O3 differed between coastal cities and inland cities. The present study could provide a scientific basis for targeted O3 pollution control in coastal and inland cities.

Total column ozone (TCO) describes the amount of ozone in the entire atmosphere. Many scholars have used the lower resolution data to study TCO in different regions, but new phenomena can be discovered using high-precision and high-resolution TCO data. This paper used the long time, high accuracy, and high-resolution MSR2 dataset (2000−2019) to analyze the spatial and temporal variation characteristics of TCO over the Yangtze River Delta Urban Agglomeration to explore the relationship between the TCO and meteorological and socio-economic factors. The correlations between the TCO and climatic factors and the driving forces of meteorological and socio-economic factors on the spatial and temporal variability of TCO were also analyzed, and different mathematical models were constructed to fit the TCO for the past 20 years and predict the future trend of the TCO. The results show the following. (1) The TCO over the study area exhibited a quasi-latitudinal distribution, following a slight downtrend during 2000−2019 (0.01 ± 0.18 DU per year) and achieved its maximum in 2010 and minimum in 2019; throughout the year, an inverted V−shaped cycle characterizes the monthly variability of TCO; TCO was significantly higher in spring than in summer and autumn than winter. (2) Precipitation and the absorbed aerosol index (AAI) had critical effects on the spatial distribution of TCO, but meteorological factors were weakly correlated with the annual variation of TCO subject to the game interactions between different external driving factors. The monthly changes in the TCO were not in synergy with that of other meteorological factors, but with a significant hysteresis effect by 3–5 months. Socio-economic factors had a significant influence on TCO over the study area. (3) The Fourier function model can well describe the history and future trend of the annual TCO over the study area. The TCO over the study area shows a fluctuating upward trend (0.27 ± 1.35 DU per year) over the next 11 years. This study enriches the theoretical and technical system of ozone research, and its results can provide the necessary theoretical basis for ozone simulation and forecasting.

In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.

Background: Air pollution is one of the most important causes of respiratory diseases that people face in big cities today. Suspended particulates, carbon monoxide, sulfur dioxide, ozone, and nitrogen dioxide are the five major pollutants of air that pose many problems to human health. We aimed to provide an approach for modeling and analyzing the spatiotemporal model of ozone distribution based on Geographical Information System (GIS). Methods: In the first step, by considering the accuracy of different interpolation methods, the Inverse distance weighted (IDW) method was selected as the best interpolation method for mapping the concentration of ozone in Tehran, Iran. In the next step, according to the daily data of Ozone pollutants, the daily, monthly, and annual mean concentrations maps were prepared for the years 2015, 2016, and 2017. Results: Spatial and temporal analysis of the distribution of ozone pollutants in Tehran was performed. The highest concentrations of O 3 are found in the southwest and parts of the central part of the city. Finally, a neural network was developed to predict the amount of ozone pollutants according to meteorological parameters. Conclusion: The results show that meteorological parameters such as temperature, velocity and direction of the wind, and precipitation are influential on O 3 concentration.

This study investigates the atmospheric concentrations, spatiotemporal distribution, the associated health risks and the ozone formation potential of benzene, toluene, ethylbenzene and xylenes (BTEX) across 33 monitoring sites of Greece over a one-year period. Samples were collected using passive diffusive samplers and analyzed by gas chromatography–mass spectrometry (GC-MS). The highest BTEX concentrations were detected during winter and autumn, particularly in urban and industrial areas such as in the Attica and Thessaloniki regions, likely due to enhanced emissions from combustion-related activities and reduced atmospheric dispersion. Health risk assessment revealed that hazard quotient (HQ) values for all compounds were within the acceptable limits. However, lifetime cancer risk (LTCR) for benzene exceeded the recommended limits in multiple regions during the colder seasons, indicating notable public health concern. Source apportionment using diagnostic ratios suggested varying seasonal emission sources, with vehicular emissions prevailing in winter and marine or industrial emissions in summer. Xylenes and toluene exhibited the highest ozone formation potential (OFP), underscoring their role in secondary pollutant formation. These findings demonstrate the need for seasonally adaptive air quality strategies, especially in Mediterranean urban and semi-urban environments.

Given the impact of greenhouse gases on the change in solar radiation and the increase in ultraviolet radiation as a result of the depletion of Ozone by these gases. This study highlighted the relationship between UVB and O3 over Libya. Since Libya is a large country with a climate that varies from one region to another, the variation in Ozone and UVB amounts varies among these regions. In the northwestern regions, UVB is low and thus O3 is high, which is the opposite of the southern regions. A strong inverse relationship was found between them, with the correlation coefficient for the periods 2005-2015 and 2016-2022 being approximately −0.8243 and −0.60796, respectively. Using spatial analysis to identify high and low areas and find the spatial correlation, an inverse correlation was observed. In addition to calculating the difference between the two periods and identifying the most accurate regions, it was found that Tripoli and its suburbs have the highest amount of UVB, while the south has less UVB due to the change in solar radiation intensity over time and the increase in greenhouse gases in the north of the country, especially in the western region.

Our in situ survey demonstrates that RNA-based sequencing of 16S rRNA amplicons is a reliable approach to qualitatively probe, at high taxonomic resolution, the changes triggered by new and existing cleaning/sanitizing strategies in the environmental microbiota in human-built environments. This approach could soon represent a fast tool to clearly define which routine sanitizing interventions are more suitable for a specific food processing environment, thus limiting the costs of special cleaning interventions and potential product loss. ABSTRACT Microbial complexity and contamination levels in food processing plants heavily impact the final product fate and are mainly controlled by proper environmental cleaning and sanitizing. Among the emerging disinfection technologies, ozonation is considered an effective strategy to improve the ordinary cleaning and sanitizing of slaughterhouses. However, its effects on contamination levels and environmental microbiota still need to be understood. For this purpose, we monitored the changes in microbiota composition in different slaughterhouse environments during the phases of cleaning/sanitizing and ozonation at 40, 20, or 4 ppm. Overall, the meat processing plant microbiota differed significantly between secondary processing rooms and deboning rooms, with a greater presence of psychrotrophic taxa in secondary processing rooms because of their lower temperatures. Cleaning/sanitizing procedures significantly reduced the contamination levels and in parallel increased the number of detectable operational taxonomic units (OTUs), by removing the masking effect of the most abundant human/animal-derived OTUs, which belonged to the phylum Firmicutes. Subsequently, ozonation at 40 or 20 ppm effectively decreased the remaining viable bacterial populations. However, we could observe selective ozone-mediated inactivation of psychrotrophic bacteria only in the secondary processing rooms. There, the Brochothrix and Pseudomonas abundances and their viable counts were significantly affected by 40 or 20 ppm of ozone, while more ubiquitous genera like Staphylococcus showed a remarkable resistance to the same treatments. This study showed the effectiveness of highly concentrated gaseous ozone as an adjunct sanitizing method that can minimize cross-contamination and so extend the meat shelf life. IMPORTANCE Our in situ survey demonstrates that RNA-based sequencing of 16S rRNA amplicons is a reliable approach to qualitatively probe, at high taxonomic resolution, the changes triggered by new and existing cleaning/sanitizing strategies in the environmental microbiota in human-built environments. This approach could soon represent a fast tool to clearly define which routine sanitizing interventions are more suitable for a specific food processing environment, thus limiting the costs of special cleaning interventions and potential product loss.