Describing the trend of ammonia, particulate matter and nitrogen oxides: The role of livestock activities in northern Italy during Covid-19 quarantine

The pandemic effect on GHG emission variation at the sub-national level and translation into policy opportunities

Greenhouse gas (GHG) emissions inventories are commonly compiled at country level to monitor national progress towards nationally or internationally agreed targets. While they can support national climate change mitigation strategies, accounting for the intra-national heterogeneity of a country can draw different conclusions directly linked to the socio-economic and environmental sub-national context. This means that more refined and accurate policies and mitigation strategies can be designed when supported by GHG inventories at sub-national scale. The differences between sub-national territorial emissive behavior can be revealed by subjecting different territories to the same stress factors. A complete GHG emissions inventory, based on the Intergovernmental Panel on Climate Change (IPCC) Guidelines, is compiled for three diverse administrative territories, in terms of scale, socio-economic contexts, and environmental conditions. By selecting three diverse sub-national contexts belonging to the same national territory - Italy - the analysis provides highly detailed information on the emissive status and behavior and delivers insights that national inventories fail to provide. The COVID-19 pandemic is considered as a stress factor; therefore, the reference years are 2019 and 2020 during which GHG emissions are detected. The study will test the capacity of sub-national GHG emission inventories, compiled by scaling the IPCC methodology to the sub-national level, to detect such differences through the lens of the pandemic. This allows obtaining detailed information and linking the pandemic effect to the GHG emissions of particular activities, which can inspire effective sub-national context-specific mitigation actions. Furthermore, we show that environmental and economic metrics are not as strictly coupled as they would appear at national level.

Revisiting the dynamics of gaseous ammonia and ammonium aerosols during the COVID-19 lockdown in urban Beijing using machine learning models

The concentration of atmospheric ammonia (NH3) in urban Beijing substantially decreased during the COVID-19 lockdown (24 January to 3 March 2020), likely due to the reduced human activities. However, quantifying the impact of anthropogenic interventions on NH3 dynamics is challenging, as both meteorology and chemistry mask the real changes in observed NH3 concentrations. Here, we applied machine learning techniques based on random forest models to decouple the impacts of meteorology and emission changes on the gaseous NH3 and ammonium aerosol (NH4+) concentrations in Beijing during the lockdown. Our results showed that the meteorological conditions were unfavorable during the lockdown and tended to cause an increase of 8.4 % in the NH3 concentration. In addition, significant reductions in NOx and SO2 emissions could also elevate NH3 concentrations by favoring NH3 gas-phase partitioning. However, the observed NH3 concentration significantly decreased by 35.9 % during the lockdown, indicating a significant reduction in emissions or enhanced chemical sinks. Rapid gas-to-particle conversion was indeed found during the lockdown. Thus, the observed reduced NH3 concentrations could be partially explained by the enhanced transformation into NH4+. Therefore, the sum of NH3 and NH4+ (collectively, NHx) is a more reliable tracer than NH3 or NH4+ alone to estimate the changes in NH3 emissions. Compared to that under the scenario without lockdowns, the NHx concentration decreased by 26.4 %. We considered that this decrease represents the real decrease in NH3 emissions in Beijing due to the lockdown measures, which was less of a decrease than that based on NH3 only (35.9 %). This study highlights the importance of considering chemical sinks in the atmosphere when applying machine learning techniques to link the concentrations of reactive species with their emissions.

A meta-analysis of particulate matter and nitrogen dioxide air quality monitoring associated with the burden of disease in sub-Saharan Africa

Exposure to air pollution is a fundamental obstacle that makes it complex to realize the Sustainable Development Goals (SDGs 3) for good health and wellbeing. It is for this reason that air pollution has been characterized as the global environmental health risk facing the current generation. The risks of air pollution on morbidity, and life expectancy are well documented. This feeds directly to the substantial body of the literature that exists regarding the burden of diseases associated with ambient air pollution. However, the bulk of this literature originates from developed countries. Whilst most of the sub-Saharan African studies extrapolate literature from developed countries to contextualize the risks of elevated air pollution exposure levels associated with the burden of disease. However, extrapolation of epidemiological evidence from developed countries is problematic given that it disregards the social vulnerability. Therefore, given this observation, it is ideal to evaluate if the monitoring executions of hazardous particulate matter and nitrogen dioxide do take into consideration the concerted necessary efforts to associate monitored air pollution exposure levels with the burden of disease. Therefore, based on this background, the current meta-analysis evaluated air quality monitoring associated with the burden of disease across sub-Saharan Africa. To this extent, the current meta-analysis strictly included peer-reviewed published journal articles from the sub-Saharan African regions to gain insight on air quality monitoring associated with the burden of disease. The collected meta-analysis data was captured and subsequently analyzed using Microsoft Excel 2019. This program facilitated the presentation of the meta-analysis data in the form of graphs and numerical techniques. Generally, the results indicate that the sub-Saharan Africa is characterized by a substantial gap in the number of regional studies that evaluate the burden of disease in relation with exposure to air quality.Implications: The work presented here is an original contribution and provides a comprehensive yet succinct overview of the monitoring associated with the burden of disease in sub-Saharan Africa. The author explores if the monitoring executions of hazardous particulate matter and nitrogen dioxide do take into considerations the concerted necessary efforts to associate monitored air pollution exposure levels with the burden of disease. The manuscript includes the most relevant and current literature in a field of study that has not received a deserving degree of research attention in recent years. This is especially true in sub-Saharan Africa, characterized by insufficient monitoring of air quality exposure concentrations.

Modeling and Analysis of Air Pollution and Environmental Justice: The Case for North Carolina's Hog Concentrated Animal Feeding Operations

BACKGROUND

Concentrated animal feeding operations (CAFOs) emit pollutants that can cause negative impacts on human health. The concentration of hog production in North Carolina raises concerns regarding the disproportionate exposure of vulnerable communities to air pollution from CAFOs.

OBJECTIVES

We investigated whether exposure to gaseous ammonia (NH 3 ) and hydrogen sulfide (H 2 S ) (in 2019) differs between subpopulations by examining demographics, including race/ethnicity, age, educational attainment, language proficiency, and socioeconomic status.

METHODS

We used an Air Monitoring Station (AMS)/Environmental Protection Agency (EPA) Regulatory Model (AERMOD)-based Human Exposure Model (version 3) to estimate ambient concentrations of NH 3 and H 2 S from hog farms in Duplin County and its surrounding counties in North Carolina and estimate subsequent exposures of communities within 50 km of Duplin County, North Carolina, or the Duplin County Region. We combined estimated exposures with 2016 American Community Summary Census data, at the block group level, using spatial analysis to investigate whether exposures to these pollutants differ by race and ethnicity, age, income, education, and language proficiency. Based on these estimations, we assessed associated exposure risks to the impacted communities and used multivariable regression modeling to evaluate the relationship between average ammonia exposures from Duplin regional hog farms and the presence of vulnerable populations.

RESULTS

The average [± standard deviation  ( SD ) ] annual estimated concentration of NH 3 and H 2 S in the Duplin County Region is 1.75 ± 2.81   μ g / m 3 and 0.0087 ± 0.014   μ g / m 3 , respectively. The maximum average annual ambient concentrations are estimated at 54.27 ± 4.12   μ g / m 3 and 0.54 ± 0.041   μ g / m 3 for NH 3 and H 2 S , respectively. Our descriptive analysis reveals that people of low income, people of color, people with low educational attainment, and the linguistically isolated in the Duplin Region are disproportionately exposed to higher levels of pollutants than the average exposure for residents. Alternatively, our statistical results suggests that after adjusting for covariates, communities of color are associated with 1.70% (95% CI: - 3.79 , 0.44) lower NH 3 concentrations per 1-SD increase. One-standard deviation increases in the adults with low educational attainment and children < 19 years of age is associated with 1.26% (95% CI: - 0.77 , 3.33) and 1.20% (95% CI: - 0.62 , 3.05) higher NH 3 exposure per 1-SD increase, respectively.

DISCUSSION

Exposures to NH 3 and H 2 S differed by race and ethnicity, educational attainment, language proficiency, and socioeconomic status. The observed associations between exposure to CAFO-generated pollutants and sociodemographic indicators differed among demographics. The disproportionate distribution of hog facilities and resulting pollutant exposures among communities may have adverse environmental and human health impacts, raising environmental justice concerns. https://doi.org/10.1289/EHP11344.

Global PM2.5 and secondary organic aerosols (SOA) levels with sectorial contribution to anthropogenic and biogenic SOA formation

This study estimates global PM_2.5 and anthropogenic and biogenic Secondary Organic Aerosols (a-SOA and b-SOA) and sources contributing to their formation. The global landscape was divided into eleven domains (North America (NAM); South America (SAM); Europe (EUR); North Africa and Middle East (NAF); Equatorial Africa (EAF); South of Africa (SAF); Russia and Central Asia (RUS); Eastern Asia (EAS); South Asia (SAS); Southeast Asia (SEA) and Australia (AUS)) and 46 cities based on varying populations. Three inventories for global emissions were considered: Community Emissions Data System, Model of Emission of Gases and Aerosol, and Global Fire Emissions Database. WRF-Chem model coupled with atmospheric reactions and the secondary organic aerosol model was employed for estimating PM_2.5, a-SOA, and b-SOA for 2018. No city attained WHO's annual PM_2.5 guideline of 5 μg/m³. Delhi, Dhaka, and Kolkata (63-92 μg/m³) in south Asia were the most polluted, and seven cities (mostly in EUR and NAM) met the WHO target IV (10 μg/m³). The highest SOA levels (2-9 μg/m³) were in the cities of SAS and Africa, but with a low SOA contribution to PM_2.5 (3-22%). However, the low levels of SOA (1-3 μg/m³) in EUR and NAM had a higher contribution of SOA to PM_2.5 (20-33%). b-SOA were consistent with the region's vegetation and forest cover. The SOA contribution was dominated by residential emissions in all domains (except in the NAF and AUS) (maximum in SAS). The non-coal industry was the second highest contributor (except in EAF, NAF, and AUS) and EUR had the maximum contribution from agriculture and transport. Globally, residential and industry (non-coal and coal) sectors showed the maximum contribution to SOA, with a-SOA and b-SOA being nearly equal. Ridding of biomass and residential burning of solid fuel is the single most action benefiting the PM_2.5 and SOA concerns.

Adaptive LASSO estimation for functional hidden dynamic geostatistical models

We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the regression coefficients are functions. The algorithm simultaneously selects the relevant spline basis functions and regressors that are used to model the fixed effects. In this way, it automatically shrinks to zero irrelevant parts of the functional coefficients or the entire function for an irrelevant regressor. The algorithm is based on an adaptive LASSO penalty function, with weights obtained by the unpenalised f-HDGM maximum likelihood estimators. The computational burden of maximisation is drastically reduced by a local quadratic approximation of the log-likelihood. A Monte Carlo simulation study provides insight in prediction ability and parameter estimate precision, considering increasing spatiotemporal dependence and cross-correlations among predictors. Further, the algorithm behaviour is investigated when modelling air quality functional data with several weather and land cover covariates. Within this application, we also explore some scalability properties of our algorithm. Both simulations and empirical results show that the prediction ability of the penalised estimates are equivalent to those provided by the maximum likelihood estimates. However, adopting the so-called one-standard-error rule, we obtain estimates closer to the real ones, as well as simpler and more interpretable models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00477-023-02466-5.

Seasonal and Spatial Variations of the Oxidative Properties of Ambient PM2.5 in the Po Valley, Italy, before and during COVID-19 Lockdown Restrictions

This study describes the chemical and toxicological characteristics of fine particulate matter (PM_2.5) in the Po Valley, one of the largest and most polluted areas in Europe. The investigated samples were collected in the metropolitan area of Milan during the epidemic lockdown and their toxicity was evaluated by the oxidative potential (OP), measured using ascorbic acid (OP^AA) and dithiothreitol (OP^DTT) acellular assays. The study was also extended to PM_2.5 samples collected at different sites in the Po Valley in 2019, to represent the baseline conditions in the area. Univariate correlations were applied to the whole dataset to link the OP responses with the concentrations of the major chemical markers of vehicular and biomass burning emissions. Of the two assays, OP^AA was found mainly sensitive towards transition metals released from vehicular traffic, while OP^DTT towards the PM carbonaceous components. The impact of the controlling lockdown restrictions on PM_2.5 oxidative properties was estimated by comparing the OP values in corresponding time spans in 2020 and 2019. We found that during the full lockdown the OP^AA values decreased to 80-86% with respect to the OP data in other urban sites in the area, while the OP^DTT values remained nearly constant.

Assessment of the exposure to PM2.5 in different Lebanese microenvironments at different temporal scales

The weak potential of using the sole outdoor concentrations to represent personal exposure to PM_2.5 is confirmed by the literature; therefore, it is important to account for a person's movements over time when estimating the short-term personal air pollution exposure within different microenvironments (MEs). This study is an example of applying an assessment method of the exposure to PM_2.5 in different microenvironments at different temporal scales. A low-cost particle counter (the Dylos 1700) was used; its performance was validated in comparison with equivalent instruments such the SidePak AM520 Personal Aerosol Monitor (R² = 0.89). This validation also provided a function to convert measured particle number concentrations (PNCs) into calculated particle mass concentrations. The 150 profiles that was collected on a minute-by-minute basis regarding PM_2.5 concentration from December 2018 to May 2021 highlight the influence of individual activities and contextual factors on the air quality, so that Lebanon's annual PM_2.5 mean (24.2 µg⁄m³) is 142% higher than the World Health Organization (WHO) annual mean guideline (10 µg⁄m³). Winter is the most polluted period due to the increased application of space heating devices. Additionally, the occurrence of dusty winds during the spring period leads to the elevated levels of dispersed PM_2.5. Simultaneously, the rural zones are more polluted than urban ones due to the usage of more traditional heating equipment, in addition to the usage of chemical products like pesticides and fertilizers in agricultural activities in such areas. Furthermore, the (outdoor-indoor-transport) MEs indicate that the transport and indoor MEs have similar levels of suspended fine particulates, while outdoor MEs are less polluted. Studies based on the personal exposure to PM_2.5 were generally applied on specific and limited places such as schools, workplaces, or residences. The study aims to shed light on the modern method in an attempt to estimate the personal exposure to PM_2.5 and to inspire similar studies to achieve the maximum efficiency.

Emission estimates and air quality simulation on Lombardy during lockdown

This paper illustrates the study carried out by ARPA Lombardia to quantify the variation in daily emissions of the main pollutants and their impacts on air quality in Lombardy during the anti-COVID-19 lockdown between the end of February and the end of May 2020. A methodology for emission estimates was developed over Lombardy for this purpose and later was extended to larger areas: the Po-basin, (LIFE PREPAIR 2020) and the entire Italy (PULVIRUS 2021). In this study, the daily emissions estimates were derived by combining data from air emission inventory of Lombardy and a set of indicators that allowed to update the estimates and describe the temporal and spatial variations of the emission sources. The calculation of emission variation was conducted for all the main pollutants (PM₁₀, NH₃, NO_x, SO₂, NMVOC) and the greenhouse gases; then, the impact on air quality concentrations was simulated by the chemical and transport model FARM, that also allows to track secondary particulate and its variability in time and space on the basis of nonlinear processes and weather conditions. The estimated emission reduction, compared to the expected average value in the absence of anti-COVID-19 measures, daily varies depending on pollutants and is mainly affected by reductions in road traffic emissions and an estimated increase in domestic heating emissions. Simulations confirm strong reductions of NO₂ atmospheric average concentrations, slightly variations of PM₁₀ averages and a potential growth of tropospheric ozone.

The impact of COVID-19 lockdown on air pollution in Europe and North America: a systematic review

BACKGROUND

Multiple studies report reductions in air pollution associated with COVID-19 lockdowns.

METHODS

We performed a systematic review of the changes observed in hazardous air pollutants known or suspected to be harmful to health, including nitrogen dioxide (NO2), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and particulate matter (PM). We searched PubMed and Web of Science for studies reporting the associations of lockdowns with air pollutant changes during the COVID-19 pandemic in Europe and North America.

RESULTS

One hundred nine studies were identified and analyzed. Several pollutants exhibited marked and sustained reductions. The strongest was NO2 (93% of 89 estimated changes were reductions) followed by CO (88% of 33 estimated pollutant changes). All NOx and benzene studies reported significant reductions although these were based on fewer than 10 estimates. About three-quarters of PM2.5 and PM10 estimates showed reductions and few studies reported increases when domestic fuel use rose during COVID-19 lockdowns. In contrast, O3 levels rose as NOx levels fell. SO2 and ammonia (NH3) had mixed results. In general, greater reductions appeared when lockdowns were more severe, as well as where baseline pollutant levels were higher, such as at low-elevation and in densely populated areas. Substantial and robust reductions in NO2, NO, CO, CO2, PM2.5, PM10, benzene and air quality index pollution occurred in association with COVID-19 lockdowns. O3 levels tended to increase, while SO2 and NH3 had mixed patterns.

CONCLUSIONS

Our study shows the profound impact of human activity levels on air pollution and its potential avoidability.

Response of atmospheric deposition and surface water chemistry to the COVID-19 lockdown in an alpine area

The effects of the COVID-19 lockdown on deposition and surface water chemistry were investigated in an area south of the Alps. Long-term data provided by the monitoring networks revealed that the deposition of sulfur and nitrogen compounds in this area has stabilized since around 2010; in 2020, however, both concentrations and deposition were significantly below the average values of the previous decade for SO₄ and NO₃. Less evident changes were observed for NH₄ and base cation. The estimated decrease of deposition in 2020 with respect to the previous decade was on average - 54% and - 46% for SO₄ and NO₃, respectively. The lower deposition of SO₄ and NO₃ recorded in 2020 was caused by the sharp decrease of SO₂ and particularly of NO_x air concentrations mainly due to the mobility restrictions consequent to the COVID-19 lockdown. The limited effects on NH₄ deposition can be explained by the fact that NH₃ emission was not affected by the lockdown, being mainly related to agricultural activities. A widespread response to the decreased deposition of S and N compounds was observed in a group of pristine freshwater sites, with NO₃ concentrations in 2020 clearly below the long-term average. The rapid chemical recovery observed at freshwater sites in response to the sharp decrease of deposition put in evidence the high resilience potential of freshwater ecosystems in pristine regions and demonstrated the great potential of emission reduction policy in producing further substantial ameliorations of the water quality at sensitive sites.

Assessing the impact of long-term exposure to nine outdoor air pollutants on COVID-19 spatial spread and related mortality in 107 Italian provinces

This paper investigates the air quality in 107 Italian provinces in the period 2014-2019 and the association between exposure to nine outdoor air pollutants and the COVID-19 spread and related mortality in the same areas. The methods used were negative binomial (NB) regression, ordinary least squares (OLS) model, and spatial autoregressive (SAR) model. The results showed that (i) common air pollutants-nitrogen dioxide (NO₂), ozone (O₃), and particulate matter (PM_2.5 and PM₁₀)-were highly and positively correlated with large firms, energy and gas consumption, public transports, and livestock sector; (ii) long-term exposure to NO₂, PM_2.5, PM₁₀, benzene, benzo[a]pyrene (BaP), and cadmium (Cd) was positively and significantly correlated with the spread of COVID-19; and (iii) long-term exposure to NO_2, O₃, PM_2.5, PM₁₀, and arsenic (As) was positively and significantly correlated with COVID-19 related mortality. Specifically, particulate matter and Cd showed the most adverse effect on COVID-19 prevalence; while particulate matter and As showed the largest dangerous impact on excess mortality rate. The results were confirmed even after controlling for eighteen covariates and spatial effects. This outcome seems of interest because benzene, BaP, and heavy metals (As and Cd) have not been considered at all in recent literature. It also suggests the need for a national strategy to drive down air pollutant concentrations to cope better with potential future pandemics.

Reconstruction of Daily Courses of SO42−, NO3−, NH4+ Concentrations in Precipitation from Cumulative Samples

It is important to study precipitation chemistry to comprehend both atmospheric and environmental processes. The aim of this study was the reconstruction of daily concentration patterns of major ions in precipitation from samples exposed for longer and differing time periods. We explored sulphates (SO42−), nitrates (NO3−) and ammonium (NH4+) ions measured in precipitation within a nation-wide atmospheric deposition monitoring network in the Czech Republic during 1980–2020. We visualised the long-term trends at selected individual years for four stations, Praha 4-Libuš (LIB), Svratouch (SVR), Rudolice v Horách (RUD) and Souš (SOU), differing in geographical location and reflecting different environments. We found anticipated time trends reflecting the emission patterns of the precursors, i.e., sharp decreases in SO42−, milder decreases in NO3− and steady states in NH4+ concentrations in precipitation. Statistically significant decreasing time trends in SO42− and NO3− concentrations in precipitation between 1990 and 2015 were revealed for the LIB and SVR sites. Spring maxima in April were found for all major ions at the LIB site and for NO3− for the SVR site, for both past and current samples, whereas no distinct seasonal behaviour was recorded for NH4+ at the RUD and SO42− at the SVR sites. By applying Bayesian modelling and the Integrated Nested Laplace Approximation approach, we were able to reconstruct the daily patterns of SO42−, NO3− and NH4+ concentrations in precipitation, which might be further utilised for a wide range of tasks, including comparison of magnitudes and shapes between stations, grouping the decomposed daily data into the ecologically motivated time periods, as well as for logical checks of sampling and measurement reliability.

Swine diets: Impact of carbohydrate sources on manure characteristics and gas emissions

Swine growers seeking to lower costs and environmental impact have turned to alternative carbohydrate feed sources. A feeding trial was conducted to determine the effect carbohydrate sources have on manure composition and gas emissions. A total of 48 gilts averaging 138 kg BW were fed diets consisting of (a) low fiber (LF) grain, or (b) high fiber (HF) aro-industrial co-product (AICP). The LF diets included corn and soybean meal (CSBM) and barley soybean meal (BSBM). The HF AICP diets were CSBM based and supplemented with one of the following materials: beet pulp; corn distillers dried grains with solubles; soybean hulls; or wheat bran. Diets were fed for 42 d with an average daily feed intake of 2.71 kg d-1. Feces and urine were collected twice daily and added to manure storage containers in which manure slurries were monitored for gas emissions and chemical properties. Manures of animals fed HF diets had significantly (P < 0.05) more excretion of solids, C, N, and organic N, but less total S compared to pigs fed the LF diets. Animals feed HF diets had significantly (P < 0.05) higher levels of ammonia, sulfide, volatile fatty acids, and phenols in manure compared to pigs fed the LF diets. Manure of animals fed HF diets had 30% (P < 0.05) lower NH3 and 17% lower hydrogen sulfide emissions; however, fiber had no impact on odor emissions. Based on the partitioning of nutrients, animals fed HF fiber diets had increased manure retention for C and N but decreased levels of N gas emissions and manure S. There were little differences in manure and gas emissions for animals fed LF diets, but the source of HF AICP diets had a significant impact on manure composition and gas emissions.

Ammonia Concentration in Ambient Air in a Peri-Urban Area Using a Laser Photoacoustic Spectroscopy Detector

Measuring ammonia from the environmental air is a sensitive and prioritized issue due to its harmful effects on humans, ecosystems, and climate. Ammonia is an environmental pollutant that has an important role in forming secondary inorganic aerosols, the main component of fine particulate matter concentrations in the urban atmosphere. Through this study, we present a gas analyzer that utilizes the technique of laser photoacoustic spectroscopy to measure ammonia concentration in three different sites located in Magurele, (44°20'58″ N 26°01'47″ E, 93 m altitude), Romania, from March to August 2021 at the breathing level of 1.5 m above ground. The ammonia concentrations from the ambient air were elevated in summer (mean of 46.03 ± 8.05 ppb (parts per billion)) compared to those measured in spring (18.62 ± 2.92 ppb), which means that atmospheric temperature affects ammonia concentrations. The highest mean ammonia concentrations occurred in August, with an ammonia concentration level of 100.68 ± 11.12 ppb, and the low mean ammonia concentrations occurred in March, with an ammonia level concentration of 0.161 ± 0.03 ppb. The results confirm that meteorological characteristics (i.e., temperature) and motor vehicles are major contributors to the elevated ammonia levels during the monitoring period.

An Epidemiological Study to Investigate Links between Atmospheric Pollution from Farming and SARS-CoV-2 Mortality

Exposure to atmospheric particulate matter and nitrogen dioxide has been linked to SARS-CoV-2 infection and death. We hypothesized that long-term exposure to farming-related air pollutants might predispose to an increased risk of COVID-19-related death. To test this hypothesis, we performed an ecological study of five Italian Regions (Piedmont, Lombardy, Veneto, Emilia-Romagna and Sicily), linking all-cause mortality by province (administrative entities within regions) to data on atmospheric concentrations of particulate matter (PM_2.5 and PM₁₀) and ammonia (NH₃), which are mainly produced by agricultural activities. The study outcome was change in all-cause mortality during March-April 2020 compared with March-April 2015-2019 (period). We estimated all-cause mortality rate ratios (MRRs) by multivariate negative binomial regression models adjusting for air temperature, humidity, international import-export, gross domestic product and population density. We documented a 6.9% excess in MRR (proxy for COVID-19 mortality) for each tonne/km² increase in NH₃ emissions, explained by the interaction of the period variable with NH₃ exposure, considering all pollutants together. Despite the limitations of the ecological design of the study, following the precautionary principle, we recommend the implementation of public health measures to limit environmental NH₃ exposure, particularly while the COVID-19 pandemic continues. Future studies are needed to investigate any causal link between COVID-19 and farming-related pollution.

Assessing the carbon footprint across the supply chain: Cow milk vs soy drink

Since livestock product consumption could have a significant effect on tackling climate change, in the few last years, there has been an increasing consumer demand for non-dairy alternatives. Despite plant-based beverages being considered crucial to foster the transition towards sustainable diet models, no studies have yet compared the level of emissions of plant-based beverages with animal-based ones. The present study aims at computing the carbon footprint of cow milk and that of soy drink and evaluating the carbon footprint results in the light of the substitutability of cow's milk with soy drink, analyzing the potential environmental, economic and nutritional trade-offs between the two products. Results highlight that, considering the environmental perspective, soy drink could be a valid substitute of cow milk: its production has a lower carbon footprint, allowing for the achievement of food security objectives. However, focusing on the economic and nutritional perspectives, the high average consumer price of soy drink is associated with an overall lower nutritional level. In order to reach the same nutritional value as 1 L of cow milk in terms of protein intake, the consumption of soy drink should be increased by 13%. Furthermore, soy drink consumption implies paying 66% more than for cow milk, when considering the same protein content.

Study on the Agricultural Air Pollution Aggravated by the Rural Labor Migration

In recent years, air pollution has received serious concerns from researchers, media, and the public sectors, but air pollution from agricultural production activities has not received enough attention. This paper focuses on agricultural air pollution in central China, which is aggravated by the ongoing rural labor migration trend. With a set of panel data released from Hubei and Hunan provinces in China, we adopt the mediating effect model to explore the relationship between rural labor migration and air pollution caused by agricultural activity in China. First, we use the inventory analysis method and principal component analysis method to calculate the comprehensive index of the air pollution of agriculture in 152 counties and districts from Hubei and Hunan provinces, and we empirically test the impact of labor migration on air pollution with a mediating effect model as well as carry out regional heterogeneity analysis on the pollution effect of these two provinces mentioned above. The analysis above indicates that the increase of labor migration has intensified the comprehensive index of air pollution caused by agricultural activity by changing the supply of labor force in the agricultural sector, the budget line of rural residents, and the scale of agricultural production and crop planting structure, but there is a difference in the indirect total effect between the two provinces mentioned above according to our regional heterogeneity analysis. This study is a necessary extension to studies on alleviating and controlling air pollution in China.

Comparison of ammonia air concentration before and during the spread of COVID-19 in Lombardy (Italy) using ground-based and satellite data

Several anthropogenic activities have undergone major changes following the spread of the COVID-19 pandemic, which in turn has had consequences on the environment. The effect on air pollution has been studied in detail in the literature, although some pollutants, such as ammonia (NH₃), have received comparatively less attention to date. Focusing on the case of Lombardy in Northern Italy, this study aimed to evaluate changes in NH₃ atmospheric concentration on a temporal scale (the years from 2013 to 2019 compared to 2020) and on a spatial scale (countryside, city, and mountain areas). For this purpose, ground-based (from public air quality control units scattered throughout the region) and satellite observations (from IASI sensors on board MetOp-A and MetOp-B) were collected and analyzed. For ground-based measurements, a marked spatial variability is observed between the different areas while, as regards the comparison between periods, statistically significant differences were observed only for the countryside areas (+31% in 2020 compared to previous years). The satellite data show similar patterns but do not present statistically significant differences neither between different areas, nor between the two periods. In general, there have been no reduction effects of atmospheric NH₃ as a consequence of COVID-19. This calls into question the role of the agricultural sector, which is known to be the largest responsible for NH₃ emissions. Even if the direct comparison between the two datasets shows little correlation, their contextual consideration allows making more robust considerations regarding air pollutants.

Air Quality in the Italian Northwestern Alps during Year 2020: Assessment of the COVID-19 «Lockdown Effect» from Multi-Technique Observations and Models

The effect of COVID-19 confinement regulations on air quality in the northwestern Alps is assessed here based on measurements at five valley sites in different environmental contexts. Surface concentrations of nitrogen oxides (NO and NO2), ozone (O3), particulate matter (PM2.5 and PM10), together with a thorough microphysical (size), chemical, and optical (light absorption) aerosol characterisation, complemented by observations along the vertical column are considered. Even in the relatively pristine environment of the Alps, the «lockdown effect» is well discernible, both in the early confinement phase and in late 2020. The variations observed during the first confinement period in the city of Aosta (−61% NO, −43% NO2, +5% O3, +9% PM2.5, −12% PM10, relative to average 2015–2019 conditions) are attributed to the competing effects of air pollution lockdown-induced changes (−74%, −52%, +18%, −13%, −27%, relative to the counterfactual scenario for 2020 provided by a predictive statistical model trained on past measurements) and meteorology (+52%, +18%, −11%, +25%, +20%, relative to average conditions). These changes agree well with the ones obtained from a chemical transport model with modified emissions according to the restrictions. With regard to column-integrated quantities and vertical profiles, the NO2 column density decreases by >20% due to the lockdown, whereas tropospheric aerosols are mainly influenced by large-scale dynamics (transport of secondary particles from the Po basin and mineral dust from the Sahara desert and the Caspian Sea), except a shallow layer about 500 m thick close to the surface, possibly sensitive to curtailed emissions (especially exhaust and non-exhaust particles from road traffic and fugitive emissions from the industry).

Ammonia exposure causes the disruption of the solute carrier family gene network in pigs

Ammonia is the main harmful gas in livestock houses. However, the toxic mechanism of ammonia is still unclear. Therefore, we examined the effects of ammonia exposure on different tissues of fattening pigs by histological analysis and transcriptome techniques in this study. The results showed that there were varying degrees of pathological changes in liver, kidney, hypothalamus, jejunum, lungs, spleen, heart and trachea of fattening pigs under ammonia exposure. Notably, the extent of damage in liver, kidney, jejunum, lungs, hypothalamus and trachea was more severe than that in heart and spleen. Transcriptome results showed that ammonia exposure caused changes in 349, 335, 340, 229, 120, 578, 407 and 115 differentially expressed genes in liver, kidney, spleen, lung, trachea, hypothalamus, jejunum and heart, respectively. Interestingly, the changes in solute vector (SLC) family genes were found in all 8 tissues, and the verified gene results (SLC11A1, SLC17A7, SLC17A6, SLC6A4, SLC22A7, SLC25A3, SLC28A3, SLC7A2, SLC6A6, SLC38A5, SLC22A12, SLC34A1, SLC26A1, SLC26A6, SLC27A5, SLC22A8 and SLC44A4) were consistent with qRT-PCR results. In conclusion, ammonia exposure can cause pathological changes in many tissues and organs of fattening pigs and changes in the SCL family gene network. Importantly, the SCL family is involved in the toxic mechanism of ammonia. Our findings will provide a new insight for better assessing the mechanism of ammonia toxicity.

COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

Regional Scale Impact of the COVID-19 Lockdown on Air Quality: Gaseous Pollutants in the Po Valley, Northern Italy

The impact of the reduced atmospheric emissions due to the COVID-19 lockdown on ambient air quality in the Po Valley of Northern Italy was assessed for gaseous pollutants (NO2, benzene, ammonia) based on data collected at the monitoring stations distributed all over the area. Concentration data for each month of the first semester of 2020 were compared with those of the previous six years, on monthly, daily, and hourly bases, so that pre, during, and post-lockdown conditions of air quality could be separately analyzed. The results show that, as in many other areas worldwide, the Po Valley experienced better air quality during 2020 spring months for NO2 and benzene. In agreement with the reductions of nitrogen oxides and benzene emissions from road traffic, estimated to be −35% compared to the regional average, the monthly mean concentration levels for 2020 showed reductions in the −40% to −35% range compared with the previous years, but with higher reductions, close to −50%, at high-volume-traffic sites in urban areas. Conversely, NH3 ambient concentration levels, almost entirely due the emissions of the agricultural sector, did not show any relevant change, even at high-volume-traffic sites in urban areas. These results point out the important role of traffic emissions in NO2 and benzene ambient levels in the Po Valley, and confirm that this region is a rather homogeneous air basin with urban area hot-spots, the contributions of which add up to a relatively high regional background concentration level. Additionally, the relatively slow response of the air quality levels to the sudden decrease of the emissions due to the lockdown shows that this region is characterized by a weak exchange of the air masses that favors both the build-up of atmospheric pollutants and the development of secondary formation processes. Thus, air quality control strategies should aim for structural interventions intended to reduce traffic emissions at the regional scale and not only in the largest urban areas.

Impact of the COVID-19 Outbreak on Air Quality in Korea

The COVID-19 pandemic has led countries to take action, which has included practicing social distancing or lockdown. Many cities are experiencing air quality improvements due to human activity restrictions. The purpose of this study was to compare the air quality between 2020 and the previous three years, focusing on the two cities (Seoul and Daegu) where coronavirus is spreading the fastest in Korea. Significant decreases in PM2.5, PM10, CO, and NO2 were observed in both cities. In particular, compared to the same period of 2017-2019, in March 2020, PM2.5 showed remarkable reductions of 36% and 30% in Seoul and Daegu, respectively. The effects of social distancing have maximized improvements in air quality due to reduced transboundary pollutants. The PM2.5/PM10 ratio was significantly reduced after social distancing, indicating that the contribution of traffic-related PM2.5 declined. Air quality improved overall from January to July, and the most noticeable drop in the air quality index (AQI) was observed in April. These findings indicate that relatively weak social distancing measures compared to a COVID-19 lockdown can help reduce air pollutant levels. At the same time, however, changes in air quality in the neighboring countries caused by COVID-19 control action are affecting Korea.