- 1CMCC Foundation—Euro-Mediterranean Center on Climate Change, Lecce, Italy
- 2RFF-CMCC European Institute on Economics and the Environment, Milan, Italy
- 3Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy
- 4Department of Social and Political Sciences, Bocconi University, Milan, Italy
- 5Department of Economics, University of Verona, Verona, Italy
- 6Legambiente, Rome, Italy
Air pollution is one of the main environmental health concerns globally, with particulate matter (PM) as the primary threat. While many policies address emissions from transport and industry, there is growing evidence of agriculture’s significant impact on air quality. Evaluating how intensive farming impacts PM concentrations and public health is necessary for informed policy interventions. We focus on the Po Valley (Italy), characterized by intensive agricultural practices and substantial pollution levels. Our study examines secondary inorganic aerosol (SIA) concentrations between 2013 and 2020 in Lombardy. Our findings reveal key insights into the impact of intensive farming on air pollution and public health. First, we find that ammonium salts make up over 30% of the daily particulate matter
1 Introduction
The European Environment Agency (EEA) and the World Health Organization (WHO) rank air pollution as Europe’s greatest environmental health risk. Outdoor air pollution alone is responsible for more than 327,000 premature deaths every year. Fine particulate matter with a diameter less than or equal to 2.5 μm (
In Italy, the majority of fatalities occur in the Po Valley, a densely populated and highly industrialized region of Northern Italy with some of the highest particulate matter (PM) concentrations among OECD countries (European Environment Agency, 2023a). Due to this basin’s unique geo-morphological and meteorological conditions, the chemical regimes of PM are complex, non-linear, and spatially varying, exacerbating pollution levels (Thunis et al., 2021).
Additionally, the Po Valley is a hub for intensive agricultural activities and livestock farming, resulting in high atmospheric ammonia (
Concerning both short and long-term PM components’ toxicity and their related effects on health, the existing research is still insufficient (Kinney et al., 2010; Atkinson et al., 2014; Chung et al., 2015; Wyzga and Rohr, 2015; Badaloni et al., 2017; Chen and Hoek, 2020), in particular on the role of nitrates and sulfates (Cassee et al., 2013; World Health Organization Regional Office for Europe, 2013). Some studies suggest that the inorganic components of PM might be less detrimental than the carbonaceous part (Schlesinger and Cassee, 2003), which could be up to five times more harmful (Tuomisto et al., 2008; Lelieveld et al., 2015), and than combustion aerosols in general (Park et al., 2018). However, other researchers have obtained different findings. Achilleos et al. (2017) find in their meta-analysis that the short-term association between
Numerous analyses on the role of agriculture on air pollution and on the spatiotemporal distribution of
Given that the most critical health impacts originate from PM itself, unlike previous studies, we focus on SIA, a component of PM, rather than
Our study is one of the few exploring long-range transport patterns and their impact on SIA formation in Lombardy across a long time series. One of the agricultural activities with a substantial impact on SIA formation is the broadcasting of manure. We look at the impact of manure spreading on the observed SIA levels in Milan. Finally, we evaluate the health impact of agriculture. This involves estimating the number of deaths and years of life lost attributable to exposure to ammonium salts as a fraction of
2 Materials and methods
2.1 Data
2.1.1 PM and secondary inorganic aerosol
PM may either be directly emitted into the atmosphere through biogenic or anthropogenic emissions (primary aerosol) or may indirectly result from chemical reaction processes (secondary aerosol) (Seinfeld and Pandis, 2016). In Europe’s urban environments, including the Po Valley, the secondary aerosol component of PM prevails in the total mass concentration (Larsen et al., 2012; Aksoyoglu et al., 2017; Thunis et al., 2021; Clappier et al., 2021).
Depending on the composition, secondary aerosol may be classified as secondary organic aerosol (SOA) or as secondary inorganic aerosol (SIA). Gaseous precursors of the SIA are atmospheric
In Italy,
2.1.2 Study area
Lombardy, located in Northern Italy within the Po Valley, is the region with the highest number of livestock units and intensive rearing in the country. It also stands among the leading regions in Europe in terms of livestock (Statistical Office of the European Union, 2023). Refer to Supplementary Figure S1 to compare cattle and pig livestock numbers in Lombardy with other European regions. As of the conclusion of 2021, Lombardy housed approximately 27.58% of the Italian cattle population, amounting to 1,555,372 units, and a substantial 50.55% of the Italian pig population, totaling 4,242,918 units (Pretolani and Rama, 2022). Notably, within the category of ruminants with significant dietary requirements, more than one-fourth of milk cows were found in Lombardy. However, Lombardy’s breeding farms represented only 10% and 8.84% of the Italian total for cattle and pigs, respectively. In the context of agricultural emissions of
Due to its high anthropogenic activity but also to its geographical characteristics, Lombardy is particularly subject to the accumulation of pollutants and poor air quality for prolonged periods. In particular, the presence of the Alps on the northern and western side and of the Apennines on the southern side determine weak wind conditions and frequent thermal inversion episodes, hindering atmospheric dispersion and trapping pollution to the ground (Caserini et al., 2017).
2.1.3 Data sources
In this study, we employed a wide range of data types, including air quality, meteorological conditions, livestock, crop areas, effluent dispersion, population demographics, and mortality statistics. All of these data sets are readily accessible online, with the exception of PM speciation data, which are provided by the regional environmental protection agency, Agenzia Regionale per la Protezione dell’Ambiente (ARPA Lombardia), which is in charge of collecting samples and validating the data for the territory of Lombardy in the appliance of the European Directive on Air Quality (European Union, 2008). For further details on the analytical methods followed by ARPA Lombardia and the instrumentation employed, see Supplementary Section S1. Supplementary Tables S1, S2 report the limit of detection and the uncertainty associated with the ions’ measurements.
Specifically, we have gathered daily data on air pollutants from the Open Data Lombardia portal (Regione Lombardia, 2021), with a particular focus on select pollutants and trace gases, including
Concentrations are expressed in
1. The urban background station on Pascal Street in the city of Milan, characterized by a highly urbanized environment.
2. The urban traffic station on Senato Street in the city of Milan, also in an urban setting.
3. The rural background station in Schivenoglia, situated in the Mantua province in South Eastern Lombardy, known for its predominantly agricultural surroundings.
For the sake of simplicity, we will refer to these air quality stations as follows: the background station in Milan (Pascal), the traffic station in Milan (Senato), and the rural station in Schivenoglia. Notice that ammonium salt data are available for all stations until August 2020. However, for the rural station, data prior to February 2018 is not available. In Figure 1, the geographical locations of each air quality station within the region are displayed in relation to land use categories, which include urban areas (colored in grey), agricultural areas (yellow), forests (green), wetlands (violet), and bodies of water (light blue). Notice that livestock specialization prevails in the Lombardy plain to the east of Milan, which is much more limited in the west, where the prevalent specialized crop is rice paddy (Regione Lombardia, 2019).
Figure 1. Air quality stations with respect to land use in Lombardy. Land use categories are urban areas, agricultural areas, forests, wetlands, and waters (colored in grey, yellow, green, violet, and light blue, respectively). The blue cross identifies background stations, while the red one identifies traffic stations. Map of Europe with a red rectangle over Lombardy.
Meteorological conditions, namely, wind speed and wind direction, have been sourced from weather station data available on the regional open data portal (Regione Lombardia, 2021). Since the meteorological and air quality stations are not co-located, we have associated weather conditions with air quality stations using the nearest weather station. Specifically, the nearest weather station for Milan’s background air quality station (Juvara) is located approximately 1.2 kilometers (km) away. For Milan’s traffic air quality station, the closest meteorological urban site (Brera) is situated approximately 660 m away. As for the rural air quality station, the nearest weather station is located in Sermide, a semi-urban environment in the province of Mantua, at a distance of 16 km. The lack of co-location between the monitoring stations may introduce minor measurement errors in attributing wind conditions. However, this is unlikely to significantly impact the analyses exploiting aggregate weather conditions under Section 3.2. In urban areas like Milan, small distances between stations do not typically result in major variations in wind speed and direction due to homogeneous meteorological conditions. For rural stations, such as those in the flat Manua province, distant weather stations likely still provide representative wind patterns, as topographical features have less influence on wind conditions.
Annual livestock consistencies are available on the data portal of the statistical office of the European Union (Eurostat) (Statistical Office of the European Union, 2023). Six-month data on livestock numbers are accessible through the National Data Bank of the Zootechnical Registry portal (Ministero della Salute, 2021). Regarding the spreading of livestock effluents (referred to as “spreading windows”), starting in 2016, the Regional Agency for Agricultural and Forestry Services (ERSAF) has been providing information twice a week through a bulletin that specifies permissible times for spreading within the six pedoclimatic zones in Lombardy: Alps, Western Prealps, Eastern Prealps, Western Plain, Central Plain, and Eastern Plain. We have obtained these data from the ERSAF website (Ente Regionale per i Servizi all’Agricoltura e alle Foreste, 2021). For a detailed overview of agricultural sources of ammonium salts, refer to Supplementary Section S4.
Information about land use and crop surfaces comes from a regional land use and cover database for the year 2018, known as Destinazione d’Uso dei Suoli Agricoli e Forestali (DUSAF), version 6.0 (Destination of use of agricultural and forest soils) (Regione Lombardia, 2019). Additionally, data on the regional and municipal borders for the year 2021 have been derived from the National Institute of Statistics (ISTAT) (Istituto Nazionale di Statistica, 2021). Lastly, annual population figures, observed all-cause mortality rates, and 3-year life expectancy data for the city of Milan were accessed through the Integrated Statistical System portal of the municipality (Comune di Milano, 2021).
2.2 Methods
2.2.1 Local wind patterns
Identifying sources of secondary pollutants that are not directly emitted into the atmosphere can be challenging. We employ a multifaceted approach that integrates various data sources and methods to accomplish this goal. In particular, we build upon the research conducted by Lonati and Cernuschi (2020), utilizing bivariate polar plot (BPP) and bivariate conditional probability distribution function (BCPF) analyses to investigate the relationship between daily concentrations of air pollutants and wind patterns. Notably, our analysis extends beyond
A BPP is a well-established technique for source apportionment. It represents concentration data using polar coordinates (reflecting wind direction) and radial coordinates (indicating a second numeric variable, typically wind speed). This approach provides insights into the probable distance and origin of sources that influence pollution levels at the receptor. We first partition the time series of atmospheric compound concentrations into bins based on wind speed and wind direction and then calculate averages. This method proves valuable in characterizing sources, facilitating the differentiation between diffuse, ground-level sources (such as livestock farms, residential heating, or traffic-related emissions) and point sources with buoyant emissions (including industrial plants, harbors, or airports), as demonstrated by Lonati and Cernuschi (2020). For further details, we refer to Carslaw et al. (2006), Carslaw and Ropkins (2012), Carslaw and Beevers (2013), Uria-Tellaetxe and Carslaw (2014), John H. Seinfeld (2016), and Grange and Carslaw (2019). We generate BPP and BCPF plots using the polarPlot function within the openair R package for air pollution analysis (Carslaw and Ropkins, 2012; Carslaw, 2019).
2.2.2 Long-range air mass patterns
We also employ long-range back-trajectory techniques to account for the potential influence of distant sources, given that SIA can persist in the atmosphere for up to 2 weeks and be transported. We use the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model version 5.3.2 (Stein et al., 2015) to derive 72-h air mass back-trajectories for our urban and rural background source receptors. For the simulation, trajectories are calculated daily at 00, 06, 12, and 18 universal Time Coordinated (UTC), starting from date 2013-01-01 to date 2020-08-01;at a height of 100 (Scotto et al., 2021) and 500;m (Ara Begum et al., 2005; Pekney et al., 2006; Zhao et al., 2007; Zhou et al., 2019; Scotto et al., 2021) above ground level (m a.g.l.), respectively. The HYSPLIT model, widely used for calculating atmospheric trajectories (Fleming et al., 2012), was developed by the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory. As meteorology data, we employ the National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) archived reanalysis data, with a 2.5° latitude-longitude resolution. We use the SplitR R package (Iannone, Richard, 2016) to apply the model and determine trajectories. As methods of analysis, we use the Potential Source Contribution Function (PSCF) and Concentration-Weighted Trajectory (CWT) methods, generated with the openair R package (Carslaw and Ropkins, 2012). The PSCF method provides spatially distributed conditional probabilities of highly polluting sources, based on a concentration percentile of interest (Ara Begum et al., 2005; Pekney et al., 2006; Zhao et al., 2007; Scotto et al., 2021). E.g., we apply PSCF to estimate likely sources contributing to concentrations above the 90th percentile. While PSCF relies on probabilities, the CWT method allows instead calculating a spatial weighted average of concentrations, providing complementary information on sources (Lupu and Maenhaut, 2002; Zhao et al., 2007; Masiol et al., 2015).
2.2.3 Health impacts from long-term concentration-response estimates
To evaluate the human health effects of agricultural activities that emit
AD represent the number of individuals who have experienced premature mortality due to specific causes, such as exposure to air pollution, while YLL quantify the potential lifetime loss due to specific causes (European Environment Agency, 2022). To provide a comprehensive assessment, we compute AD and YLL for both the entire adult population of Milan aged 20 and older and for groups defined by year
Deaths per year, gender, and age group
We exclude mortality from non-natural causes by applying a correction factor of 0.963 specific for the province of Milan taken from Carugno et al. (2017). The correction coefficient is constructed based on 2009–2013 mortality data and calculated excluding violent deaths.
The relative risk
where
In Eq. 2,
The number of years of life lost per year, gender, and age group
Finally, we compute yearly aggregates Eqs. 4, 5 as well as the rates of attributable deaths and years of life lost per 100,000 inhabitants Eqs. 6, 7. This approach follows methodologies used in prior research (Carugno et al., 2017; Giannini et al., 2017).
where
3 Results
3.1 Secondary inorganic aerosol’s characterization
3.1.1 Summary statistics
Our data analysis begins with an overview of key pollutant concentrations observed at the three air quality stations, as summarized in Supplementary Tables S6–S8. Notably,
In winter, particulate levels between Milan and Schivenoglia are positively correlated. Specifically,
In the region,
3.1.2 Ammonium salts in PM
SIA is, on average, a third of
Table 1. Percentiles of ammonium salts’ share in
Figure 2. Frequency distribution plots of ammonium nitrates (
Figure 3. Time series of annualized daily mean concentration levels (2013–2020) for
While there are no regulatory limits for
3.2 The nexus between ammonium salts and agriculture
To investigate the temporal correlation of agriculture activities with the SIA levels measured in Milan, we calculate the cross-correlation between the
Figure 4. Pearson’s cross-correlation plots between pollutants’ time series in Milan air quality stations and Schivenoglia for winter months only (December, January, February). (A) Lagged
The formation of SIA depends not only on agricultural activities, as seen above but also on meteorological conditions and other pollutants. We analyze the seasonal polar plots of SIA and its precursors in Milan and the rural areas. Supplementary Figures S10–S12 show the average pollutant concentrations by wind conditions for each station and season. Strong seasonal patterns are observed for all stations, and a high variability characterizes each pollutant throughout the year. In particular, winter stands out as the season with the highest concentration values for
To derive insights from specific concentration ranges, we exploit the BCPF technique developed by Uria-Tellaetxe and Carslaw (2014), which puts together the conditional probability function approach with that of polar plots. This way, the BCPF plot associates a probability to a specific concentration level bin, eventually identifying potential sources. We first focus on the worse case pollution episodes and compare the BCPF plots for the range of concentrations
Figure 5. BPP for 2013–2020 mean levels of
Based on recent relevant literature, air pollution sources may be associated with specific concentration ranges (Uria-Tellaetxe and Carslaw, 2014). After having focused on concentration peaks, we use BCPF to find evidence of transported versus locally produced SIA and its precursors. In addition, we examine the average concentrations of pollutants below and above specific threshold values to investigate the wind conditions linked to recommended and undesirable air quality levels. In Figures 6–8, we show BCPF plots for
Figure 6. BCPF bins for
Figure 7. BCPF bins for
Figure 8. BCPF bins for
Finally, we generate plots of ratios between the cumulative mass of ammonium salts and the total mass of
Concerning Milan’s SIA levels, various graphical analyses have suggested a significant impact from regions known for high livestock density. One of the agricultural activities with a substantial impact on SIA formation is the broadcasting of livestock manure (Pohl et al., 2022; Wyer et al., 2022). We look at the correlation between this activity and the observed SIA levels in Milan, analyzing what happened before and after a spreading event in the Western Plain to which Milan belongs. Figures 9A,B show
Figure 9. Scatter plots of ammonium salt levels in
After having looked at local contributions to air pollution, we turn to long-range contributions. Back-trajectory techniques have been applied in the Po Valley context in relation to different pollutants and time spans (Sogacheva et al., 2007; Hamed et al., 2007; Masiol et al., 2012; Diémoz et al., 2019; Scotto et al., 2021). We derive 3-day back-trajectories at the urban background receptor location in Milan at 500;m a.g.l. between 01 January 2013 and 29 February 2020, at 0:00, 6:00, 12:00, and 18:00 UTC, excluding months when COVID-19 restrictions were put in place across Europe (Eurostat, 2022).
In Supplementary Figures S22–S24, we show the Potential Source Contribution Function (PSCF) and the Concentration Weighted Trajectory (CWT) seasonal plots for
Figure 10. Seasonal gridded 72-h back-trajectory
3.3 Health impacts attributable to exposure to ammonium salts in Milan
As a final stage, we shift our focus to evaluating the impact of agriculture-related inorganic
Table 2. Annual attributable deaths (AD), years of life lost (YLL), attributable deaths rate (ADR), and years of life lost rate (YLLR) every 100,000 inhabitants in Milan (2013–2019) due to long-term exposure to ammonium salts as a fraction of
Table 3. 2013–2019 mean attributable deaths (AD) by quinquennial age and gender in Milan due to long-term exposure to ammonium salts as a fraction of
Table 4. 2013–2019 mean years of life lost (YLL) by quinquennial age and gender in Milan due to long-term exposure to ammonium salts as a fraction of
4 Discussion
In the region of Lombardy, located in Northern Italy, the agricultural sector stands as the primary source of ammonia (
We find that, at the air quality stations analyzed and especially in rural areas, SIA accounts for a significant portion of the overall
In winter, pollution levels between cities are positively correlated, implying similar trends regardless of local sources, while SIA in Milan’s background air quality station correlates less to
We also find suggestive evidence of the relationship between manure spreading and SIA. Our observations indicate a 2 micrograms per cubic meter of air (
Annually, 589 [446–866] deaths and 6,951 [5,267–10,222] years of life, equivalent to 43 [33–64] and 511 [387–751] every 100,000 inhabitants, are lost on average in Milan due to pollution linked to agricultural activities that could be curbed with technological abatement measures without reducing production. Although referring to
We acknowledge several limitations to our study. The estimation method of ammonium salts is based on the assumption that salts are pure, though this may not be the case, biasing our estimates. We apply long-term concentration-response functions to determine the number of nonaccidental deaths attributable to exposure to ammonium salts, even though on the one hand they were originally constructed for the
Our research adds to an expanding body of literature that explores the impact of agriculture on air quality, specifically focusing on their association rather than causality. However, investigating the causal effect in future studies would be of great interest. Our study also provides insights into the health consequences of secondary air pollution in an urban environment heavily influenced by agricultural activities. Furthermore, our research highlights the importance of PM speciation data and underscores the value of making this information accessible to the public for research purposes and for raising awareness of the complexity of PM pollution.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: 10.17632/2mzdnzfwmt.1.
Author contributions
SR: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing–original draft, Writing–review and editing. JL: Conceptualization, Investigation, Writing–review and editing, Methodology. FG: Investigation, Writing–review and editing, Data curation. MM: Conceptualization, Investigation, Writing–review and editing, Methodology. DD: Investigation, Resources, Supervision, Writing–review and editing, Data curation.
Funding
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research has received funding from the Fondazione CARIPLO under the project “INHALE—Impact on human Health of Agriculture and Livestock Emissions,” and from the "GRINS-Growing Resilient, INclusive and Sustainable" project (GRINS PE00000018).
Acknowledgments
We thank ARPA Lombardia for providing the
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2024.1369678/full#supplementary-material
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Keywords: coarse particulate matter (PM10), secondary inorganic aerosol (SIA), ammonium salts, agriculture, back-trajectories, human health, attributable deaths (AD), years of life lost (YLL)
Citation: Renna S, Lunghi J, Granella F, Malpede M and Di Simine D (2024) Impacts of agriculture on
Received: 12 January 2024; Accepted: 06 June 2024;
Published: 11 July 2024.
Edited by:
Mounia Tahri, National Centre for Nuclear Energy, Science and Technology, MoroccoReviewed by:
Mauro Masiol, Ca’ Foscari University of Venice, ItalySolomon Giwa, Olabisi Onabanjo University, Nigeria
Copyright © 2024 Renna, Lunghi, Granella, Malpede and Di Simine. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Stefania Renna, stefania.renna@cmcc.it