Global anthropogenic chemicals loads on the environment and the associated chemical footprint and planetary boundaries: a high-resolution regional study

Introduction

Nowadays, the interest in green chemistry is growing significantly and even developing countries have begun to adopt «green» policies [1]. Such an interest in this area is explained by the fact that the chemical industry, on the one hand, for decades has traditionally been considered as one of the main sources of environmental impact [2] and, on the other hand, it is one of the most important tools for solving environmental problems and reaching the Sustainable Development Goals. In Agenda 21 (item 19) adopted at the UN Conference on Environment and Development (Rio de Janeiro, 1992), it was noted that «A substantial use of chemicals is essential to meet the social and economic goals of the world community and today’s best practice demonstrates that they can be used widely in a cost-effective manner and with a high degree of safety» [3]. Green chemistry [4] is one of the most important and useful tools for integrating the principles of sustainable development and green economy into chemistry and chemical industry [5]. It should be noted that in present world, green chemistry refers to such processes and the resulting chemical products, the use of which leads to minimizing the negative impact on humans and the environment. The need of assessment and minimization of impact of chemicals on the environment is reflected by Sustainable Development Goals approved by the UN [6] – Goal 12 «Ensure sustainable consumption and production patterns» with regard to item 12.4 «By 2020, achieve the environmentally sound management of chemicals and all wastes throughout their life cycle, in accordance with agreed international frameworks, and significantly reduce their release to air, water and soil in order to minimize their adverse impacts on human health and the environment».

Substances of main concern of chemists worldwide are heavy metals. Heavy metals, such as copper (Cu), nickel (Ni), lead (Pb), vanadium (V), etc., can pose a serious hazard to the environment and human health [7]. Heavy metals are toxic even at very low concentrations (<1 ppb) [8]. Several metals (for example, Cu, Ni and V) are the integral part of biological systems and processes and must be present in optimum concentrations [9] as they are the vital trace elements for the proper development of organisms [9]. In excessive amounts, they are dangerous. Other metals are highly toxic and according to the International Agency for Research on Cancer possess carcinogenic and mutagenic properties, so they pose a serious danger to the human body even at low concentrations [10]. Excessive amounts of lead can negatively affect the central nervous system and cerebrum [11], [12]. When entering the environment, metals relatively easy accumulate in soils, but slowly and hardly move away, for example, has been predicted that, in the soils of the lowland under the most favorable conditions of soil self-purification, half-life of Ni, Cr and Pb before the removal from the soil is much more than 200 years, while for Zn and Cu, it makes about 90 and 150 years [13].

The substantial part of this research is dealing with the pollution of the Baltic Sea. In Russia, the regions that have access to the Baltic Sea are Saint-Petersburg and the Leningrad region. It should also be noted that the Baltic Sea is one of the most polluted seas in the world [14] and its ecological status is affected by relatively slow water renewal and salinity gradient increasing from zero to 2.5%. Salinity and temperature stratification limit water exchange and cause oxygen depletion, usually at the bottom in deep areas. The catchment area of the Baltic Sea includes forests (54%), agricultural areas (26%), areas occupied by ponds and wetlands (20%) and industrial areas and lands of settlements – 4% [10], [15]. The catchment area of the Baltic Sea is heavily affected by humans due to the presence of industrial areas and lands of settlements with the population of about 85 million people, including 5 million living in Saint-Petersburg and 2 million – in the Leningrad region.

There are numerous methods [16], [17] used for the evaluation and the prediction of the pollution of various environmental subsystems. For the evaluation of the soil pollution with heavy metals, including comprehensive assessment of pollution, the following approaches are used: Nemerow pollution index (NPI) [18], enrichment factor (EF) [19], potential ecological risk index (PERI) [20], geoaccumulation index (Igeo) [21] and contamination security index (CSI) [13]. Many scientists [22] use geographic information systems (GIS) technologies to analyze and visualize spatial data on heavy metal pollution. For example, Carr et al. [23] have developed maps of heavy metals pollution with the use of GIS, Shan et al. [24] used GIS to study the accumulation of heavy metals in soils, Amous and Hassan applied GIS to assess heavy metal content in water [25]. To predict environmental pollution and to assess the impact of various factors such as regulatory decisions or climate change, modeling is used including composition of a mobile data acquisition terminal and a web-based information system.

In this study, due to the necessity to obtain data on heavy metal content in water, soil and air, based on available data on emissions, it was decided to use the USEtox model, in particular for the simulation of the redistribution of chemicals between such environmental compartments as urban air and air of settlements, fresh waters and coastal sea waters, ocean, agricultural soils and other soils. The USEtox model was chosen because it is available free and its structure can be modified (model is executed in MS Excel), in addition there is a positive experience in using this model in combination with GIS [26]. To assess the complex risk of heavy metal pollution of the environmental compartments, the authors proposed the approach to chemical footprint assessment based on the use of hygienic standards.

Calculation method

The authors have developed the numerical method for the calculation of «chemical footprint» based on the method described in [27], which was modified resulting in potential to apply it for the atmosphere and soil [28]. The authors propose the following definition of «chemical footprint» based on the theory of the ecological space: «The chemical footprint is a measure of such quantities of components of the environment [urban air VairU,sys and air of settlements VairC,sys (m³), fresh water Vfr.water,sys and coastal sea waters Vsea.water,sys (m³), agricultural soils Magr.soil,sys and other soils Mnat.soil,sys (kg)], which are sufficient for the dilution of anthropogenic chemicals to concentrations below the established limits [maximum permissible concentration (MPC), tentative safe exposure level (TSEL) or approximate permissible level (APL)]». It should be noted that in order to simplify the calculations, it is proposed to use hygienic standards for hazardous substances content in different environmental subsystems as a boundary values [29].

In previously published works [29] quite successful attempts of assessment of chemical footprint for individual substances (in particular mercury) have already been described. Having finalized the previously used formula taking into account the adopted definition of «chemical footprint», the authors proposed the following formula for calculating chemical footprint for the i-th chemical:

where: V_airU,i, V_airC,i – volumes of urban air and air of settlements required for the dilution of the i-th chemical, released into the atmosphere, to the established limits; V_fr.water,i, V_sea.water,i – volumes of fresh and sea water required for dilution of the i-th chemical, discharged into the water bodies, to the established limits (m³); M_agr.soil,i, M_nat.soil,i – quantity of agricultural soils and other soils (kg) required for the distribution of the i-th chemical to the established limits.

However, in the environment, the chemical substances are not isolated from each other, and the assessment of their environmental impact should take this fact into consideration. Another complication is resulting from the fact that even in case the calculations demonstrate the concentrations of individual toxic substances being below the established limits, the probability of the complex impact of the mixture of chemicals should not be neglected.

It should be noted that in the case of simultaneous presence of several chemicals (in our case, heavy metals) their chemical footprints are added up, which automatically implies the additivity principle in relation to heavy metals. For array I of heavy metals, taking into account the additivity principle, the total chemical footprint is equal to:

In this research, the boundaries of the system under consideration are the boundaries of the cells in the grid with the resolution of 0.5°×0.5°, where six compartments are allocated: urban and rural air, fresh water, coastal sea water (if available), agricultural soils and other soils.

Air volumes V_airU,i, V_airC,i are calculated by the equation:

where: m_airU,i, m_airC,i – the amount of the i-th metal contained in the urban or atmospheric air (kg); Lim_a,i – MPC (or TSEL if there is no MPC) of the i-th metal in the air of settlements (mg/m³).

Volumes of water V_fr.water,i, V_seawater,i are calculated by the equation:

where: m_fr.water,i, m_sea.water,i – the amount of the i-th metal contained in freshwater or sea water bodies (kg); Lim_w,i – MPC (or TSEL if there is no MPC) of the i-th metal in fishery waters (mg/m³).

Quantities of agricultural soils M_agr.soul,i and other soils M_nat.soil,i are calculated by the equation:

where: m_agr.soil,i, m_nat.soil,i –the amount of the i-th metal contained in agricultural soils or other soils (kg); L_ims,i – MPC (or TSEL if there is no MPC) of the i-th metal in the soil (mg/kg).

Amounts of metals in the environmental compartments were calculated using the model proposed by the authors, which was obtained by the USEtox model modification. The USEtox model was modified to calculate the content of metals in the compartments of 0.5°×0.5° cells, in particular, the modifications considered the fact that the cells may not contain other soils as well as seawaters. The changes also made it possible to take into account the directional transport of chemicals with fresh waters.

Algorithm for the assessment of the directional transport of metals with water flows

To take into account the spatial differentiation caused by the transport of freshwater flows, the system is proposed to be presented as a set of 0.5°×0.5° cells. Figure 1 shows the block diagram of model processes occurring in the j-th cell of the studied geographical area of 0.5°×0.5°, taking into account the transport of freshwater flows on some territory (geographical area) with certain boundaries.

Fig. 1:

The block diagram of heavy metals life cycle in freshwater.

The authors proposed the algorithm of the calculation of rate constants of chemical substances transport in the hydrosphere using GIS (k^transb), which allowed taking into account the spatial differentiation in this subsystem while calculating chemical load and chemical footprint. The calculation algorithm is based on use of global databases containing the required data for the entire globe with a spatial grid of 0.5°×0.5° resolution; in particular, such data are collected in the river basins database Simulated Topological Networks (STN-30p) ^[1]. The topology of this river grid is derived from a digital landscape model and agreed with independent data on river routing (direction of transport of water flows in the cell). The average multi-year values of amount of transported water from the j-th cell per year (m³/year), average annual precipitation (mm/year), irrigation in the i-th cell (m³/year) and rural and urban population for the j-th cell for a grid of 0.5°×0.5° are available on the University of New Hampshire website ^[2]; the average annual temperature (K) and wind speed (m/s) in the j-th cell for the i-th cell for a grid of 0.5°×0.5° are taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) ^[3] website, and agricultural land shares are taken from the EarthStat resource developed as a result of the collaboration between the Universities of Minnesota and British Columbia ^[4]. The changes are described in more detail in [30]. The coefficients of transport inside the cell (k^migr) were calculated using the above-mentioned databases and standard equations from the USEtox model. However, the modification described in the present work were not sufficient to obtain acceptable accuracy. The difference between the total mass of chemicals (for example Al³⁺), calculated for the Leningrad region plus global level (Europe) using the modified model, and the total mass of chemicals, calculated using the USEtox model, was about 4%, which required additional studies. During additional studies, it was found that using a scale of 0.5°×0.5° might lead to appearance of cells with absence of any soils. In particular, for the Leningrad region such cells are cells that fall on Lake Ladoga. For such cells additional changes were needed: due to «RESIDENCE TIME of air over urban scale» (TAU.aU) is equal to 0, the removal factor «TRANSFER air – continental scale» (k.aU.aC) calculated as 1/TAU.aU should also be forced to zero for these cells. Otherwise, the system gives an error or incorrect calculations. For reasons similar to those described above, «MEAN atmospheric deposition rate» (KDEPmean.U), mean rate constant for removal from atmosphere (ktot.U) and removal factor «TRANSFER air – urban scale» (k.aC.aU) should also be forced to zero. When calculating the transfer coefficient of the atmospheric air from the local to the global level, it is necessary to zero item taking into account the volume of transported urban air (because there are no cities as well as urban air for these cells). Resulting in these additional modifications, the estimated difference in the amount, both of Al³⁺ and other metal compounds, fell within 0.5%.

As can be seen from Fig. 1, the transfer of chemicals from the cell to the cell occurs only through the hydrosphere. However, this connection can also occur through the airflow and the transport of industrial products and waste. This omission leads to the need to refine the model.

Another source of error in modeling may be the factor that the authors do not take into account seasonal changes in the chemicals transport rate that directly affect to the redistribution of heavy metals.

Sources of emission and discharge data

The official reporting data of the enterprises presented on the website https://onv.fsrpn.ru/ were used as a source of data on emissions of heavy metals. This database is administered by the Federal Service for Supervision of Natural Resources of the Russian Federation. For this study, the authors have analyzed the reporting of more than 200 enterprises. Additionally, data on discharges were used. As there is no open reporting of the enterprises on discharges, data of the Federal Water Resources Agency (http://voda.mnr.gov.ru/activities/list.php?part=45) on standards of permissible impact on water bodies of the Leningrad region were taken for calculations. The calculation was carried out as follows:

1. Establishment of the boundaries of the water bodies basins according to the reference points specified in the document;

2. Proportional distribution of discharges according to number of 0.5°×0.5°cells, which fall into the boundaries of the basins.

This calculation is approximate because it is assumed that discharges of any object are equal to maximum permissible amounts of pollutants, also sources of discharge are not exactly defined.

The assessments were carried out for Cu²⁺, Ni²⁺, V⁵⁺, Sn²⁺, Pb²⁺ and additionally for Al³⁺ which, along with heavy metals, represents a certain concern in terms of environmental impact, since in the Leningrad region there is an aluminum production facility that releases certain amounts of it into the environment.

When collecting information on the release of heavy metals into the environmental subsystems, the following types of facilities posing the greatest impact on the environment have been identified. They are metallurgical enterprises, machine-building enterprises, enterprises of electronic industry and various metal goods manufacture. It is obvious that various metals are involved in the above-mentioned production processes, so it causes their entry into the environment.

However, in addition to the above, heavy metals are also present in the emissions of printing and publishing enterprises, as well as of those producing pulp, paper and packaging.

Discussion of the results

The calculations showed that the chemical pollution by heavy metals estimated as chemical footprint by the eq. 2 is the greatest problem for the Leningrad region in terms of pollution of water bodies, both fresh (see Fig. 2) and sea (see Fig. 3).

Fig. 2:

The chemical footprint for fresh water of the Leningrad region.

Fig. 3:

The chemical footprint for sea water of the Leningrad region.

In the figures, cells in which the chemical footprint exceeds 1 are marked in red, which means that in these areas there is an unacceptable risk of pollution of the environment with heavy metals.

Such cells are located in areas where there are large industrial zones, and heavy metals enter the environment with the emissions of the enterprises engaged in the production of alumina, aluminum, limestone and gallium.

Conclusion

The methodology described in this paper makes it possible to identify with a high accuracy the territories potentially subjected to negative impact of chemicals as well as the elements of the environment for the territories under consideration.

Due to its’ universality and the possibility to use the GIS data bases, this methodology might be applied to the territories of different scales: from a regional and/or country scale to the global one. It should be also pointed out that the methodology of the calculation of chemical footprints needs relatively small amounts of initial data, is able to determine chemical footprints for a large number (hundreds and even thousands) of chemicals, simultaneously present in the territory under consideration.

The computer model was applied to assess chemical footprints for Cu²⁺, Ni²⁺, V⁵⁺, Sn²⁺, Pb²⁺ and additionally for Al³⁺ which, along with heavy metals, represents a certain concern in terms of environmental impact in the Leningrad region of the Russian Federation, the technogenic sources of pollution in this region being metallurgical enterprises, machine-building enterprises, enterprises of electronic industry and various metal goods manufacture. The results obtained made it possible to identify the areas with an unacceptable risk of pollution of the environment with heavy metals.

Thus, the developed approach could help to estimate the chemical load within the planetary boundaries, as well as to monitor the degree of SDGs fulfillment [31]. It should be noted that similar techniques are actively used for the EIA in the EU [32].

In this paper the interconnection between the cells was considered only through the hydrosphere. However, the interconnection can take place also through air flow as a way of transport of industrial products and waste. It is evident that seasonal changes of chemicals transport rates should exert an influence on the process of heavy metals redistribution. In this paper an average state was considered, whereas the process in reality is transient. This can reflect in significant seasonal changes in ecological hazard distribution. These factors might be a potential source of the model inaccuracy which will be eliminated in future development of the model. The further development of the high-resolution regional study methodology will take into consideration the directions of the atmospheric and ocean flows and seasonal changes. The developed methods can be used for predictive assessments of the environment impact during the construction of industrial facilities [32].