The interactive effects of ambient air pollutants-meteorological factors on confirmed cases of COVID-19 in 120 Chinese cities

The association between the scarlet fever and meteorological factors, air pollutants and their interactions in children in northwest China

Scarlet fever (SF) is an acute respiratory transmitted disease that primarily affects children. The influence of meteorological factors and air pollutants on SF in children has been proved, but the relevant evidence in Northwest China is still lacking. Based on the weekly reported cases of SF in children in Lanzhou, northwest China, from 2014 to 2018, we used geographical detectors, distributed lag nonlinear models (DLNM), and bivariate response models to explore the influence of meteorological factors and air pollutants with SF. It was found that ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), temperature, pressure, water vapor pressure and wind speed were significantly correlated with SF based on geographical detectors. With the median as reference, the influence of high temperature, low pressure and high pressure on SF has a risk effect (relative risk (RR) > 1), and under extreme conditions, the dangerous effect was still significant. High O3 had the strongest effect at a 6-week delay, with an RR of 5.43 (95%CI: 1.74,16.96). The risk effect of high SO2 was strongest in the week of exposure, and the maximum risk effect was 1.37 (95%CI: 1.08,1.73). The interactions showed synergistic effects between high temperatures and O3, high pressure and high SO2, high nitrogen dioxide (NO2) and high particulate matter with diameter of less than 10 μm (PM10), respectively. In conclusion, high temperature, pressure, high O3 and SO2 were the most important factors affecting the occurrence of SF in children, which will provide theoretical support for follow-up research and disease prevention policy formulation.

Air Pollution in Relation to COVID-19 Morbidity and Mortality: A Large Population-Based Cohort Study in Catalonia, Spain (COVAIR-CAT)

INTRODUCTION

Evidence from epidemiological studies based on individual-level data indicates that air pollution may be associated with coronavirus disease 2019 (COVID-19) severity. We aimed to test whether (1) long-term exposure to air pollution is associated with COVID-19-related hospital admission or mortality in the general population; (2) short-term exposure to air pollution is associated with COVID-19-related hospital admission following COVID-19 diagnosis; (3) there are vulnerable population subgroups; and (4) the influence of long-term air pollution exposure on COVID-19-related hospital admissions differed from that for other respiratory infections.

METHODS

We constructed a cohort covering nearly the full population of Catalonia through registry linkage, with follow- up from January 1, 2015, to December 31, 2020. Exposures at residential addresses were estimated using newly developed spatiotemporal models of nitrogen dioxide (NO23), particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5), particulate matter ≤10 μm in aerodynamic diameter (PM10), and maximum 8-hr-average ozone (O3) at a spatial resolution of 250 m for the period 2018-2020.

RESULTS

The general population cohort included 4,660,502 individuals; in 2020 there were 340,608 COVID-19 diagnoses, 47,174 COVID-19-related hospital admissions, and 10,001 COVID-19 deaths. Mean (standard deviation) annual exposures were 26.2 (10.3) μg/m3 for NO2, 13.8 (2.2) μg/m3 for PM2.5, and 91.6 (8.2) μg/m3 for O3. In Aim 1, an increase of 16.1 μg/m3 NO2 was associated with a 25% (95% confidence interval [CI]: 22%-29%) increase in hospitalizations and an 18% (10%-27%) increase in deaths. In Aim 2, cumulative air pollution exposure over the previous 7 days was positively associated with COVID-19-related hospital admission in the second pandemic wave (June 20 to December 31, 2020). Associations of exposure were driven by exposure on the day of the hospital admission (lag0). Associations between short-term exposure to air pollution and COVID-19-related hospital admission were similar in all population subgroups. In Aim 3, individuals with lower individual- and area-level socioeconomic status (SES) were identified as particularly vulnerable to the effects of long-term exposure to NO2 and PM2.5 on COVID-19-related hospital admission. In Aim 4, long-term exposure to air pollution was associated with hospital admission for influenza and pneumonia: (6%; 95% CI: 2-11 per 16.4-μg/m3 NO2 and 5%; 1-8 per 2.6-μg/m3 PM2.5) as well as for all lower respiratory infections (LRIs) (18%; 14-22 per 16.4-μg/m3 NO2 and 14%; 11-17 per 2.6-μg/m3 PM2.5) before the COVID-19 pandemic. Associations for COVID-19-related hospital admission were larger than those for influenza or pneumonia for NO2, PM2.5, and O3 when adjusted for NO2.

CONCLUSIONS

Linkage across several registries allowed the construction of a large population-based cohort, tracking COVID-19 cases from primary care and testing data to hospital admissions, and death. Long- and short-term exposure to ambient air pollution were positively associated with severe COVID-19 events. The effects of long-term air pollution exposure on COVID-19 severity were greater among those with lower individual- and area-level SES.

A Review of the Interactive Effects of Climate and Air Pollution on Human Health in China

PURPOSE OF REVIEW

Through a systematic search of peer-reviewed epidemiologic studies, we reviewed the literature on the human health impacts of climate and ambient air pollution, focusing on recently published studies in China. Selected previous literature is discussed where relevant in tracing the origins.

RECENT FINDINGS

Climate variables and air pollution have a complex interplay in affecting human health. The bulk of the literature we reviewed focuses on the air pollutants ozone and fine particulate matter and temperatures (including hot and cold extremes). The interaction between temperature and ozone presented substantial interaction, but evidence about the interactive effects of temperature with other air pollutants is inconsistent. Most included studies used a time-series design, usually with daily mean temperature and air pollutant concentration as independent variables. Still, more needs to be studied about the co-occurrence of climate and air pollution. The co-occurrence of extreme climate and air pollution events is likely to become an increasing health risk in China and many parts of the world as climate changes. Climate change can interact with air pollution exposure to amplify risks to human health. Challenges and opportunities to assess the combined effect of climate variables and air pollution on human health are discussed in this review. Implications from epidemiological studies for implementing coordinated measures and policies for addressing climate change and air pollution will be critical areas of future work.

Remote sensing-based seasonal surface urban heat island analysis in the mining and industrial environment

Cooling spaces have an optimistic influence on surface urban heat islands (SUHI). Blue spaces benefit from balancing the changing climate and heat variations. Because of the rapid deforestation and SUHI increase, the climate is gradually changing in Paschim Bardhhaman, West Bengal state, India. Paschim Bardhhaman has two sectors: specifically, Durgapur is the main industrial centre and Asansol has coal mines. This investigation aims to categorize spatiotemporal variations and seasonal differences in cooling spaces and their influence on SUHI, land use and land cover (LULC), and thermal differences using Landsat datasets for the years 1992, 2004, 2012, and 2022 in summer and winter. The coal mining and industrial range decreased from 10,391.92 (1992) to 3591.1 ha (2022), respectively. Open pit mining distresses fresh water by heavy water uses in ore processing, and mining water was applied to excerpt minerals. Among the two sub-divisions, the blue space amount was higher in Asansol because mining actions were higher in Asansol than in Durgapur. The open vegetation volume has reduced from 46,441.03 (1992) to 25,827.55 ha (2022) and dense vegetation has erased from 7368.02 (1992) to 15,608.56 ha (2022). Dense vegetation improved because of heavy precipitation in those regions. Mostly, Raghunathpur, Saraswatiganja, Bhagabanpur, Bistupur, Paschim Gangaram, Garkilla Kherobari, and Gourbazar have dense vegetation. The outcomes similarly demonstrate that the total built-up part has increased by 8412.82 ha in between 30 years. The built-up zone changes near the southeast and western Paschim Bardhhaman district. Those region needs appropriate attention and planning to survive soon.

Short-term exposure to air pollution and hospital admission after COVID-19 in Catalonia: the COVAIR-CAT study

BACKGROUND

A growing body of evidence has reported positive associations between long-term exposure to air pollution and poor COVID-19 outcomes. Inconsistent findings have been reported for short-term air pollution, mostly from ecological study designs. Using individual-level data, we studied the association between short-term variation in air pollutants [nitrogen dioxide (NO2), particulate matter with a diameter of <2.5 µm (PM2.5) and a diameter of <10 µm (PM10) and ozone (O3)] and hospital admission among individuals diagnosed with COVID-19.

METHODS

The COVAIR-CAT (Air pollution in relation to COVID-19 morbidity and mortality: a large population-based cohort study in Catalonia, Spain) cohort is a large population-based cohort in Catalonia, Spain including 240 902 individuals diagnosed with COVID-19 in the primary care system from 1 March until 31 December 2020. Our outcome was hospitalization within 30 days of COVID-19 diagnosis. We used individual residential address to assign daily air-pollution exposure, estimated using machine-learning methods for spatiotemporal prediction. For each pandemic wave, we fitted Cox proportional-hazards models accounting for non-linear-distributed lagged exposure over the previous 7 days.

RESULTS

Results differed considerably by pandemic wave. During the second wave, an interquartile-range increase in cumulative weekly exposure to air pollution (lag0_7) was associated with a 12% increase (95% CI: 4% to 20%) in COVID-19 hospitalizations for NO2, 8% (95% CI: 1% to 16%) for PM2.5 and 9% (95% CI: 3% to 15%) for PM10. We observed consistent positive associations for same-day (lag0) exposure, whereas lag-specific associations beyond lag0 were generally not statistically significant.

CONCLUSIONS

Our study suggests positive associations between NO2, PM2.5 and PM10 and hospitalization risk among individuals diagnosed with COVID-19 during the second wave. Cumulative hazard ratios were largely driven by exposure on the same day as hospitalization.

Ozone Pollution and Acute Exacerbation of Asthma in Residents of China: An Ecological Study

Purpose

The evidence for a causal relationship between high-level ozone (O3) exposure and acute exacerbation of asthma among adults is limited, and the conclusions are less definitive.

Patients and methods

Here we collected the daily data on asthma cases, O3 exposure, and meteorological factors from 2010 to 2016 in Shijiazhuang, China. We investigated the risk of asthma exacerbation associated with high-level ozone exposure using a polynomial distributed lag model (PDLM). Using a generalized additive model (GAM), we estimated the interactive effects between O3 and other pollutants as well as meteorological factors on asthma exacerbation.

Results

A total of 7270 patients with asthma were enrolled from 22 governmental hospitals in 13 counties. Each 10 μg/m3 increase in O3 concentration on the exacerbation of asthma was associated with a 1.92% (95% CI = 0.80-3.03%) higher risk of asthma exacerbation on day lag 7. The cumulative risk of O3 on asthma exacerbation increased by 18.9% (95% CI = 12.8-25.4%) on the 14th day. High consecutive levels of O3 increase the risk of asthma exacerbation, and the interactive effect of O3 and sulfur dioxide (SO2) appears before the exacerbation onset.

Conclusion

These findings suggested that O3 should be an important risk factor for asthma exacerbation, and health benefits in reducing asthma exacerbation risk would be gained with continued efforts to improve the air quality in China.

Impact of COVID-19 lockdown on water quality in China during 2020 and 2022: two case surges

The COVID-19 severely affected the world in 2020. Taking the two outbreaks in China in 2020 and 2022 as examples, the spatiotemporal changes in surface water quality levels and COD_Mn and NH₃-N concentrations were analyzed, and the relationships between the variations in the two pollutants and environmental and social factors were evaluated. The results showed that during the two lockdowns, due to the total water consumption (including industrial, agricultural, and domestic water) decreased, the proportion of good water quality increased by 6.22% and 4.58%, and the proportion of polluted water decreased by 6.00% and 3.98%, the quality of water environment has been improved significantly. However, the proportion of excellent water quality decreased by 6.19% after entering the unlocking period. Before the second lockdown period, the average COD_Mn concentration exhibited a "falling, rising, and falling" trend, while the average NH₃-N concentration changed in the opposite direction. The correlation analysis revealed that the increasing trend of pollutant concentrations was positively correlated with longitude and latitude, and weakly correlated with DEM and precipitation. A slight decrease trend in NH₃-N concentration was negatively correlated with the population density variation and positively correlated with the temperature variation. The relationship between the change in the number of confirmed cases in provincial regions and the change in pollutant concentrations was uncertain, with positive and negative correlations. This study demonstrates the impact of lockdowns on water quality and the possibility of improving water quality through artificial regulation, which can provide a reference basis for water environmental management.

Machine learning-based country-level annual air pollutants exploration using Sentinel-5P and Google Earth Engine

Climatic condition is triggering human health emergencies and earth's surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth's health. Nitrogen Dioxide (NO₂), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human's health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO₂ variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50-60% NO₂ was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.

The impact of meteorological factors on the spread of COVID-19

Background

Clinical studies suggest that warmer climates slow the spread of viral infections. In addition, exposure to cold weakens human immunity.

Aim

This study describes the relationship between meteorological indicators, the number of cases, and mortality in patients with confirmed coronavirus disease 2019 (COVID-19).

Patients and Methods

This was a retrospective observational study. Adult patients who presented to the emergency department with confirmed COVID-19 were included in the study. Meteorological data [mean temperature, minimum (min) temperature, maximum (max) temperature, relative humidity, and wind speed] for the city of Istanbul were collected from the Istanbul Meteorology 1^st Regional Directorate.

Results

The study population consisted of 169,058 patients. The highest number of patients were admitted in December (n = 21,610) and the highest number of deaths (n = 46) occurred in November. In a correlation analysis, a statistically significant, negative correlation was found between the number of COVID-19 patients and mean temperature (rho = -0.734, P < 0.001), max temperature (rho = -0.696, P < 0.001) or min temperature (rho = -0.748, P < 0.001). Besides, the total number of patients correlated significantly and positively with the mean relative humidity (rho = 0.399 and P = 0.012). The correlation analysis also showed a significant negative relationship between the mean, maximum, and min temperatures and the number of deaths and mortality.

Conclusion

Our results indicate an increased number of COVID-19 cases during the 39-week study period when the mean, max, and min temperatures were consistently low and the mean relative humidity was consistently high.

High altitude Relieves transmission risks of COVID-19 through meteorological and environmental factors: Evidence from China

Existing studies reported higher altitudes reduce the COVID-19 infection rate in the United States, Colombia, and Peru. However, the underlying reasons for this phenomenon remain unclear. In this study, regression analysis and mediating effect model were used in a combination to explore the altitudes relation with the pattern of transmission under their correlation factors. The preliminary linear regression analysis indicated a negative correlation between altitudes and COVID-19 infection in China. In contrast to environmental factors from low-altitude regions (<1500 m), high-altitude regions (>1500 m) exhibited lower PM2.5, average temperature (AT), and mobility, accompanied by high SO2 and absolute humidity (AH). Non-linear regression analysis further revealed that COVID-19 confirmed cases had a positive correlation with mobility, AH, and AT, whereas negatively correlated with SO2, CO, and DTR. Subsequent mediating effect model with altitude-correlated factors, such as mobility, AT, AH, DTR and SO2, suffice to discriminate the COVID-19 infection rate between low- and high-altitude regions. The mentioned evidence advance our understanding of the altitude-mediated COVID-19 transmission mechanism.

Ambient air pollution and COVID-19 risk: Evidence from 35 observational studies

BACKGROUND AND AIMS

The coronavirus disease 2019 (COVID-19) pandemic is severely threatening and challenging public health worldwide. Epidemiological studies focused on the influence of outdoor air pollution (AP) on COVID-19 risk have produced inconsistent conclusions. We aimed to quantitatively explore this association using a meta-analysis.

METHODS

We searched for studies related to outdoor AP and COVID-19 risk in the Embase, PubMed, and Web of Science databases. No language restriction was utilized. The search date entries were up to August 13, 2021. Pooled estimates and 95% confidence intervals (CIs) were obtained with random-/fixed-effects models. PROSPERO registration number: CRD42021244656.

RESULTS

A total of 35 articles were eligible for the meta-analysis. For long-term exposure to AP, COVID-19 incidence was positively associated with 1 μg/m3 increase in nitrogen dioxide (NO2; effect size = 1.042, 95% CI 1.017-1.068), particulate matter with diameter <2.5 μm (PM2.5; effect size = 1.056, 95% CI 1.039-1.072), and sulfur dioxide (SO2; effect size = 1.071, 95% CI 1.002-1.145). The COVID-19 mortality was positively associated with 1 μg/m3 increase in nitrogen dioxide (NO2; effect size = 1.034, 95% CI 1.006-1.063), PM2.5 (effect size = 1.047, 95% CI 1.025-1.1071). For short-term exposure to air pollutants, COVID-19 incidence was positively associated with 1 unit increase in air quality index (effect size = 1.001, 95% CI 1.001-1.002), 1 μg/m3 increase NO2 (effect size = 1.014, 95% CI 1.011-1.016), particulate matter with diameter <10 μm (PM10; effect size = 1.005, 95% CI 1.003-1.008), PM2.5 (effect size = 1.003, 95% CI 1.002-1.004), and SO2 (effect size = 1.015, 95% CI 1.007-1.023).

CONCLUSIONS

Outdoor air pollutants are detrimental factors to COVID-19 outcomes. Measurements beneficial to reducing pollutant levels might also reduce the burden of the pandemic.

Spatial and temporal changes analysis of air quality before and after the COVID-19 in Shandong Province, China

Due to the COVID-19 pandemic outbreak, the home quarantine policy was implemented to control the spread of the pandemic, which may have a positive impact on the improvement of air quality in China. In this study, Google Earth Engine (GEE) cloud computing platform was used to obtain CO, NO2, SO2 and aerosol optical depth (AOD) data from December 2018-March 2019, December 2019-March 2020, and December 2020-March 2021 in Shandong Province. These data were used to study the spatial and temporal distribution of air quality changes in Shandong Province before and after the pandemic and to analyze the reasons for the changes. The results show that: (1) Compared with the same period, CO and NO2 showed a decreasing trend from December 2019 to March 2020, with an average total change of 4082.36 mol/m2 and 167.25 mol/m2, and an average total change rate of 4.80% and 38.11%, respectively. SO2 did not have a significant decrease. This is inextricably linked to the reduction of human travel production activities with the implementation of the home quarantine policy. (2) The spatial and temporal variation of AOD was similar to that of pollutants, but showed a significant increase in January 2020, with an average total amount increase of 1.69 × 107 up about 2.54% from December 2019 to March 2020. This is attributed to urban heating and the reduction of pollutants such as NOx. (3) Pollutants and AOD were significantly correlated with meteorological data (e.g., average temperature, average humidity, average wind speed, average precipitation, etc.). This study provides data support for atmospheric protection and air quality monitoring in Shandong Province, as well as theoretical basis and technical guidance for policy formulation and urban planning.

Positive association between outdoor air pollution and the incidence and severity of COVID-19. A review of the recent scientific evidences

In June 2020, we published a review focused on assessing the influence of various air pollutants on the transmission of SARS-CoV-2, and the severity of COVID-19 in patients infected by the coronavirus. The results of most of those reviewed studies suggested that chronic exposure to certain air pollutants might lead to more severe and lethal forms of COVID-19, as well as delays/complications in the recovery of the patients. Since then, a notable number of studies on this topic have been published, including also various reviews. Given the importance of this issue, we have updated the information published since our previous review. Taking together the previous results and those of most investigations now reviewed, we have concluded that there is a significant association between chronic exposure to various outdoor air pollutants: PM2.5, PM10, O3, NO2, SO2 and CO, and the incidence/risk of COVID-19 cases, as well as the severity/mortality of the disease. Unfortunately, studies on the potential influence of other important air pollutants such as VOCs, dioxins and furans, or metals, are not available in the scientific literature. In relation to the influence of outdoor air pollutants on the transmission of SARS-CoV-2, although the scientific evidence is much more limited, some studies point to PM2.5 and PM10 as potential airborne transmitters of the virus. Anyhow, it is clear that environmental air pollution plays an important negative role in COVID-19, increasing its incidence and mortality.

Associations of acute exposure to airborne pollutants with COVID-19 infection: evidence from China

The outbreak of COVID-19, caused by SARS-CoV-2, has spread across many countries globally. Greatly, there are limited studies concerned with the effect of airborne pollutants on COVID-19 infection, while exposure to airborne pollutants may harm human health. This paper aimed to examine the associations of acute exposure to ambient atmospheric pollutants to daily newly COVID-19 confirmed cases in 41 Chinese cities. Using a generalized additive model with Poisson distribution controlling for temperature and relative humidity, we evaluated the association between pollutant concentrations and daily COVID-19 confirmation at single-city level and multicity levels. We observed a 10-μg/m³ rise in levels of PM_2.5 (lag 0-14), O₃ (lag 0-1), SO₂ (lag 0), and NO₂ (lag 0-14) were positively associated with relative risks of 1.050 (95% CI: 1.028, 1.073), 1.011 (1.007, 1.015), 1.052 (1.022, 1.083), and 1.094 (1.028, 1.164) of daily newly confirmed cases, respectively. Further adjustment for other pollutants did not change the associations materially (excepting in the model for SO₂). Our results indicated that COVID-19 incidence may be susceptible to airborne pollutants such as PM_2.5, O₃, SO₂, and NO₂, and mitigation strategies of environmental factors are required to prevent spreading.

Cytokine fields and the polarization of the immune response

Certain cells that participate in the immune response are known to become polarized in their production of cytokines. It is postulated that, after initial polarization at the site of antigenic encounter, the different types of cell arriving at this site are induced to conform to the local cytokine field, implying that they share common regulatory circuits. As they migrate, these cells might, in turn, spread the particular cytokine field. Therefore, the field is 'infectious' in nature. Propagation of the cytokine field must be regulated somehow. The invasion of the cytokine field into an organ or the entire body could have major immunological consequences.