Characterisation of atmospheric pollution near an industrial site with a biogas production and combustion plant in southern Italy

https://doi.org/10.1016/j.scitotenv.2020.137220Get rights and content

Highlights

  • Air quality was investigated near a biogas production and combustion plant.

  • Biomass burning was the main source for CO, NOX, nanoparticles, and PM2.5.

  • Plant's contribution to NO, SO2, accumulation mode particles and nitrate were found.

  • PM2.5 source apportionment showed relevant secondary contributions.

  • Biogas plant contributes to specific odorous compounds like DMA in PM2.5.

Abstract

Although biogas production can have some benefits, there is a research gap on potential influence of biogas plant emissions on local air quality, thus an accurate and comprehensive evaluation of impacts of this technology is needed. This study deals with this issue by means of a characterisation of air pollution near an industrial area including a biogas production (from biomass) and combustion plant located in South Italy. The methodology consists in advanced statistical analysis on concentration of gaseous pollutants, particles concentration and size distribution in number and mass, and PM2.5 chemical composition. High-temporal resolution measurements, supported by ancillary meteorological parameters, and source apportionment of PM2.5 using Positive Matrix Factorization (PMF) receptor model, are performed. The integrated approach provides the emissive picture consisting in different anthropogenic sources (i.e. traffic, biomass burning, and industrial facilities) with particular focus on biogas plant emissions. Results showed that CO and nitrogen oxides were influenced by vehicular traffic and biomass combustion, however, a contribution of the plant to NO was observed. SO2 was influenced mainly by transport from the industrial zone, but a second local contribution compatible with the emissions of the biogas plant was detected. Number particle concentrations were analysed in four size ranges: nanoparticles (D < 0.05 μm), ultrafine particles (D < 0.3 μm), accumulation (0.3 < D < 1 μm) and coarse particles (D > 1 μm). Nanoparticles and ultrafine particles were mainly influenced by vehicular traffic and biomass burning, instead, a contribution of the plant was individuated in the accumulation mode. PMF5 identified the contribution of six sources: crustal (14.7% ± 2.1% of measured PM2.5); marine aerosol (aged) (12.9% ± 2.3%); biomass burning (32.8% ± 1.4%); secondary sulphate (19.7% ± 2.4%); primary industrial emissions (5.4% ± 2.3%); traffic and secondary nitrate (17.0% ± 3.9%). The plant is likely to contribute to both sources, the industrial and the traffic plus secondary nitrate.

Introduction

Biogas sector has a key role in fulfilling EU environmental targets, obtaining several incentives especially in the period 2010–2013 (Benato et al., 2017). By the end of 2014, there were >17,000 active biogas plants in Europe, with the largest number of them located in Germany and Italy (Raboni and Urbini, 2014). Several economic and environmental advantages of biogas production from agricultural, municipal, and industrial wastes could be considered: sustainable energy production (bioenergy) with a low carbon impact compared to other conversion technologies (Chrebet and Martinka, 2012; Cecchi and Cavinato, 2015); creation of green jobs; promotion of local economic stability, and reduction of water and air pollution including GHG emissions (Kuo and Dow, 2017; O'Neill and Nuffer, 2011). On the other hand, biogas plants are hardly accepted by population due to their environmental and health issues, however, in some cases perceived risks could significantly reduce sharing local spread of benefits (Capodaglio et al., 2016).

The production process consists in anaerobic fermentation of organic material (feedstock), like energy crops, agricultural residues, animal and municipal wastes, in digester tanks to produce a mixture of methane (50–75%), carbon dioxide (25–50 vol%), water vapour (5–10 vol%), oxygen (<2 vol%), nitrogen (2 vol%), ammonia (<1 vol%), hydrogen (<1 vol%), hydrogen sulphide (<5–10 mg/m3); moreover, it may contain mercaptans (Dupont and Accorsi, 2006). Quality and quantity of biogas will be affected by many parameters, including pH, temperature, feed composition, loading rate, mixing condition, reactor design, and residence time. The most common use of biogas is as fuel in endothermic engines for the co-generation of electricity and heat (Combined Heat and Power, CHP). The digestate can be applied as agricultural fertilizer or as compost (after opportune treatments). Also, biogas can be upgraded to biomethane or biodiesel making it interchangeable to natural gas in terms of methane content and its utilisations (i.e. as vehicle fuel or for domestic heating).

There are scarce scientific researches related to the quality of raw biogas, upgrading technologies, potential influence of the emissions on local air quality. Therefore, an accurate and comprehensive evaluation of impacts of this technology is an interesting research issue. This type of plant presents risks related to the possible contamination of the soil and aquifers, while from the point of view of air pollution, there are risks associated with accidental releases of hydrogen sulphide (Chrebet and Martinka, 2012; Benato et al., 2017). Quantification of emissions from the entire biogas production chain is a challenge because the only continuous source is the off-gas from the gas utilisation unit while other emissions are discontinuous and diffusive (Reinelt et al., 2017). Representative estimates of total plant emissions by performing long-term measurements in all phases of production chain are still lacking. On-site and ground-based remote sensing approaches are the main methods that need a harmonisation process.

In evaluating climate impact of biogas industry, N2O can have a significant role exceeding those of CH4 and CO2 if the GTP-100 (Global Temperature Potential up to 100 years) is considered (Iordan et al., 2016). The primary emission of particles is low compared to the combustion of other fuels (including biomasses, coal or heavy fuels), however, it is possible to have secondary aerosol formation due to emissions of nitrogen oxides and sulphur oxides (Paolini et al., 2018). Typology of raw material influences final emissions, up to factor 3–4 for CO, CO2 and NOX, and up to a factor 11 for SO2 (Börjesson and Berglund, 2006). Other compounds produced are VOCs, whose emissions are 40% lower than natural gas, and formaldehyde, which is formed by an incomplete combustion of methane, resulting in a 2% contribution to the total and it has a similar emission pattern to natural gas (Gallego et al., 2016). Finally, biogas from biomass, unlike landfill biogas, has zero or very low chlorine content, so there is no significant formation of dioxins. Particular attention should be paid towards undesired emissions (mainly methane, nitrous oxide and ammonia) related to biomass storage and digestate management that represent critical steps (Paolini et al., 2018).

The review of Freiberg et al. (2018) points out that this type of plant may have significant odorous impacts related to different operations: reception and storage of organic biomass, energy conversion of biogas, treatment and storage of digestate. Such odorous emissions can result in an increased risk due to atmospheric pollution perceived by the population (Claeson et al., 2013). Technical solutions (i.e. cover all manipulation areas, ventilation of contaminated air into bio-filter) and an odor management plan are needed especially for plants close to residential areas, minimizing negative impact on life quality of local inhabitants (Vanek, 2011).

Considering the scarcity of information regarding impact of this kind of plants to atmospheric pollutants, the aim of this work is to present a characterisation of air pollution near an industrial area including a biogas production (from biomass) and combustion plant located near an urban residential area in South Italy.

The study is based on measurements of gaseous compounds, particles concentration (and their size distributions) in number and mass, supported by acquisition of ancillary meteorological parameters. Further, chemical analysis of daily PM2.5 samples was done to perform source apportionment of PM2.5 using Positive Matrix Factorization (PMF) receptor model. Results increase scientific knowledge of atmospheric pollution processes in a complex emissive context consisting in different anthropogenic sources (i.e. traffic, industrial facilities) and of the role of local meteorology.

Section snippets

Measurement site

The city of Sarno (17,061 inhabitants, municipality area of 40 km2, 30 m a.s.l.) is situated along the Monte Saro foothills, in South of Italy (Fig. S1). The specific orography conditions the atmospheric circulation, limiting the inflows of air masses from North and East (due to the Apennine Arc) and Tyrrhenian Sea breezes (due to the Monte Somma Vesuvio), and making the winds generally weak (<2 m/s). The study area is located at the NW of the municipality of Sarno, at about 1.5 km from the

Source apportionment approach

The PM2.5 chemical composition data were used for a source apportionment study based on the Positive Matrix Factorization (EPA-PMF5.0) receptor model, widely used in scientific literature to evaluate different contribution sources to aerosol concentration (Viana et al., 2008; Belis et al., 2015; Cesari et al., 2016b). The receptor model used is based on the application of the Multilinear Engine (ME-2). The input variables were classified using the Signal-to-Noise (S/N) criteria (Paatero and

Gaseous pollutants concentrations observed

Gaseous concentrations, reported as average and hourly maximum (Table S2), are all within the thresholds (or the long-term objectives) of the European legislation (Directive 2008/50/CE). However, this aspect is indicative because measurements are not taken for a full year as demanded by the legislation. Although the limits and/or target values of the legislation were not exceeded, various peaks of concentration were observed, some of which were likely associated with local sources. The

Conclusions

An intensive sampling campaign was carried out to characterize the composition of atmosphere close to the boundary of a biogas (from biomass) production and combustion plant area in the Sarno municipality (South Italy).

Vehicular traffic and biomass combustion (from agricultural activities and domestic heating) influenced significantly CO and nitrogen oxides. Emissions of NO from the biogas production and combustion plant have been individuated. SO2 concentration was due predominantly to

Declaration of competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The financial and logistic support of the Sarno Municipality is gratefully acknowledged. Special thanks to Mr. Antonio Sorrentino for hosting our mobile laboratory in his property and to Mrs. Francesca Volpi (Metropolitan Museum of NY) for her help in chemical analysis.

References (50)

  • D. Contini et al.

    Characterization and source apportionment of PM10 in an urban background site in Lecce

    Atmos. Res.

    (2010)
  • D. Contini et al.

    Comparison of PM10 concentrations and metal content in three different sites of the Venice Lagoon: an analysis of possible aerosol sources

    J. Environ. Sci.

    (2012)
  • D. Contini et al.

    Source apportionment of size-segregated atmospheric particles based on the major water-soluble components in Lecce (Italy)

    Sci. Total Environ.

    (2014)
  • D. Contini et al.

    Application of PMF and CMB receptor models for the evaluation of the contribution of a large coal-fired power plant to PM10 concentrations

    Sci. Total Environ.

    (2016)
  • E. Diapouli et al.

    Evolution of air pollution source contributions over one decade, derived by PM10 and PM2.5 source apportionment in two metropolitan urban areas in Greece

    Atmos. Environ.

    (2017)
  • A. Dinoi et al.

    Comparison of atmospheric particle concentration measurements using different optical detectors: potentiality and limits for air quality applications

    Measurement

    (2017)
  • L. Dupont et al.

    Explosion characteristics of synthesised biogas at various temperature

    J. Hazard. Mater.

    (2006)
  • E. Gallego et al.

    Impact of formaldehyde and VOCs from waste treatment plants upon the ambient air nearby an urban area (Spain)

    Sci. Total Environ.

    (2016)
  • X. Ge et al.

    Atmospheric amines part I. A review

    Atmos. Environ.

    (2011)
  • C. Iordan et al.

    Life-cycle assessment of a biogas power plant with application of different climate metrics and inclusion of near-term climate forcers

    J. Environ. Manag.

    (2016)
  • F. Ledoux et al.

    Contributions of local and regional anthropogenic sources of metals in PM2.5 at an urban site in northern France

    Chemosphere

    (2017)
  • E. Merico et al.

    Influence of in-port ships emissions to gaseous atmospheric pollutants and to particulate matter of different sizes in a Mediterranean harbour in Italy

    Atmos. Environ.

    (2016)
  • E. Merico et al.

    Atmospheric impact of ship traffic in four Adriatic-Ionian port-cities: comparison and harmonization of different approaches

    Transp. Res. Part D: Transp. Environ.

    (2017)
  • A. Nicosia et al.

    Field study of a soft X-ray aerosol neutralizer combined with electrostatic classifiers for nanoparticles size distribution measurements

    Particuology

    (2018)
  • P. Paatero et al.

    Discarding or down weighting high-noise variables in factor analytic models

    Anal. Chim. Acta

    (2003)
  • Cited by (23)

    • Strategies for enhanced microbial fermentation processes

      2022, Biomass, Biofuels, Biochemicals: Microbial Fermentation of Biowastes
    • Source term estimation with deficient sensors: Traceability and an equivalent source approach

      2021, Process Safety and Environmental Protection
      Citation Excerpt :

      The receptor models shows very good performance overall while have difficulties to quantify the sources of industrial activities with reasonable uncertainty (Belis et al., 2015). The receptor models have been applied to individual industrial emissions such as coal-fired power plant (Contini et al., 2016) and biogas production plant (Merico et al., 2020), although there are few applications to complex industrial sites, such as CIPs. This is because to make the CMB model solvable, the number of pollutant species has to be larger than the number of sources, whilst in CIPs, the emission sources to be monitored are far more than pollutant species whose reliable concentration measurements and source composition profiles are rarely available.

    • Spectroscopic insight into the pH-dependent interactions between atmospheric heavy metals (Cu and Zn) and water-soluble organic compounds in PM<inf>2.5</inf>

      2021, Science of the Total Environment
      Citation Excerpt :

      This demonstrated WSOCs were surely the key composition in PM2.5. These numbers relative to OC and WSOCs coincided with other situations, for example, were similar to the data reported in Merico et al. (2020). Furthermore, the high WSOCs/OC ratio might imply the presence of a relevant secondary fraction in OC.

    View all citing articles on Scopus
    View full text