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Case studyThe experimental setup for the field studies that provided the bacterial population and weather data used here was previously described by Belias et al. [9]. Briefly, baby spinach and lettuce plants were spray-inoculated with E. coli and S. enterica (Salmonella) onto field plots established in Davis, CA (University of California, Plant Sciences Field Research Facility); Freeville, NY (Homer C. Thompson Research Farm, Cornell University); and Murcia, Spain (La Matanza Research Farm). The spinach and lettuce varieties were selected based on their suitability for baby leaf production: lettuce var. Tamarindo, and spinach var. Acadia F1 and Seaside F1. Four replicate trials at different times of the regional growing season were carried out per location. The plants were spray-inoculated with a 104 CFU/mL cocktail of rifampin-resistant strains of commensal E. coli and attenuated S. enterica serovar Typhimurium (Salmonella), and samples were collected for bacterial cell quantification by plate counts on selective and differential media at 0, 4, 8, 24, 48, 72 and 96 h post-inoculation. Concurrent with leaf sample collection, weather variables (temperature, relative humidity (RH), solar radiation intensity, and wind velocity) were recorded hourly for the respective field locations. The hourly dew point (DP) was calculated as a function of both the hourly temperature and RH.Model for persister formation on plantsMathematical modeling to characterize the switch rate from a non-persister bacterial cell (hereafter termed “normal cell”) to a persister cell in the phyllosphere under laboratory conditions was performed as described in our previously published study [24]. Briefly, persister cell fractions were quantified in culturable EcO157 populations after inoculation onto young lettuce plants cultivated in plant growth chambers. Persister cells recovered from the lettuce phyllosphere were identified using the antibiotic lysing method [23]. The greatest persister fraction in the EcO157 population on lettuce in our laboratory investigation above was observed during population decline on leaf surfaces of plants left to dry after inoculation. Using mathematical modeling, we calculated the switch rate from an EcO157 normal to persister cell on dry lettuce plants based on these data [24]. Importantly, our laboratory conditions mimicked inoculation conditions in which E. coli arrived via water on leaves, the surfaces of which progressively dried like under prevailing weather conditions in the field.Based on the main dynamic observed in the field study data [9] and building on our previous study [24], we assumed that the total enteric pathogen population is composed of (i) non-persister (normal) cells consisting of two sub-populations, characterized by fast (n1) (CFU/100g) and slow (n2) (CFU/100g) decay, and (ii) the persister population, leading to the following model from Munther et al. [24]:$$frac{{dn_1}}{{dt}} = – theta _{n_1}n_1 – alpha _dn_1 + beta _dleft( {1 – sigma } right)hat p,$$
(1a)
$$frac{{dn_2}}{{dt}} = – theta _{n_2}n_2 – alpha _dn_2 + beta _dsigma hat p,$$
(1b)
$$frac{{dhat p}}{{dt}} = – mu _{hat p}hat p – beta _dhat p + alpha _dleft( {n_1 + n_2} right),$$
(1c)
$$n_1left( 0 right) = n_{10},n_2left( 0 right) = n_{20},, hat pleft( 0 right) = widehat {p_0},$$
(1d)
where (theta _{n_i})(1/h) is the death rate of the normal cells (subscript i = 1 for fast and i = 2 for slow), (hat p) (CFU/100 g) represents the persister cell population at time t (h), (mu _{hat p}) (1/h) reflects the persister population inactivation rate, αd (1/h) is the switch rate from normal to persister state, βd (1/h) is the switch rate from persister to the normal state, and σ ∈ (0,1) is a constant, describing the fraction of persister cells switching back to the normal, slowly decaying state. Equation (1a) and (1b) reflect the assumption that times between switching states are exponentially distributed, using the expected values (frac{1}{{alpha _d}}) (h) and (frac{1}{{beta _d}}) (h) of the respective distributions.Lacking data for potential persister populations from the field trials, we assumed the persister population is a fraction 1  > k  > 0 of the tail population, as observed in Munther et al. [24]. Regarding the model above, this implies that (hat p approx kn_2) for (t ge t^ ast), where (t^ ast approx frac{1}{{theta _{n_1}}}) (the time scale of survival for the fast-decaying population (n1)). In accord with bi-phasic decay, for (t ge t^ ast), the main dynamics for slow decaying population (n2) is dictated by (- theta _{n_2}n_2) in Eq. (1b). This suggests that the effective switch rates from n2 to (hat p) and from (hat p) back to n2 balance, so that (beta _dsigma hat p approx alpha _dn_2) in Eq. (1b). Following these ideas, we simplified the model in Eq. (1a)–(1d) to:$$frac{{dn_1}}{{dt}} = – theta _{n_1}n_1 – alpha _dn_1,$$
(2a)
$$frac{{dn_2}}{{dt}} = – theta _{n_2}n_2,$$
(2b)
$$frac{{dhat p}}{{dt}} = – theta _{hat p}hat p + alpha _dn_1,$$
(2c)
$$n_1left( 0 right) = n_{10},n_2left( 0 right) = n_{20},, hat pleft( 0 right) = widehat {p_0},$$
(2d)
where we ignored (beta _dleft( {1 – sigma } right)hat p) in (1a) since the decay rate ((theta _{n_1})) dominates. Also, by setting (theta _{hat p} = mu _{hat p} + beta _d(1 – sigma )), and using (beta _dsigma hat p approx alpha _dn_2), we obtained Eq. (2c). Furthermore, because (hat p approx kn_2) for (t ge t^ ast), (theta _{hat p} approx) (theta _{n_2}).In particular, the assumption that (hat p approx kn_2) for (t ge t^ ast) characterizes the switch rate from normal to persister cells, αd, as (alpha _d approx kalpha), where α is a hypothetical switch rate assuming that the population is composed only of fast decaying normal cells (n1) and a hypothetical persister cell population (p). In this case, the hypothetical population p starts small at (widehat {p_0}), initially increases due to switching from population n1 and then slowly decays as the n1 population is effectively inactivated (i.e., the tail of the total population is comprised entirely of p). From this perspective we utilized the following equations:$$frac{{dn_1}}{{dt}} = – theta _{n_1}n_1 – alpha n_1,$$
(3a)
$$frac{{dp}}{{dt}} = alpha n_1 – theta _pp.$$
(3b)
$$n_1left( 0 right) = n_0,, pleft( 0 right) = widehat {p_0},$$
(3c)
For mathematical justification regarding the relationship (alpha _d approx kalpha), please see the appendix (Supplementary Information).The utility of the relationship (alpha _d approx kalpha), is twofold. First, we used model fitting (Eqs. (3a)–(3c)) to determine α from the respective field study data [9]. Note that using Eqs. (3a)–(3c), we actually fit for (theta _{n_1}), θp, and α using the field study data [9]. Please reference the “model fitting procedure” section as well as the appendix for details concerning the unique determination of the aforementioned parameters, i.e., the practical identifiability of these parameters, and justification regarding the legitimacy of measured tail populations relative to the respective field trial data [9]. Second, because we wanted to examine Spearman’s correlations (corr) between αd and various weather factors, given a particular weather factor (vec w) across trials (i = 1, ldots ,n), let k be the maximum persister fraction (of the tail) across these n trials, that is, for each i, we have (alpha _{d_i} approx k_ialpha _i), so (alpha _{d_i} lesssim kalpha _i). Thus kαi represents the maximum persister switch rate for each trial i, and since corr((kvec alpha ,vec w)) =corr((vec alpha ,vec w)), we conducted the correlation analysis with the fitted α values in lieu of the actual persister switch rate αd.The assumptions behind our approach are summarized below:

A.

The tails of pathogen populations surviving on plants in the field study [9] are comprised of some fraction k ∈(0,1) of persister cells since their decay rate is quite small and they remain culturable.

B.

Because (alpha _d approx kalpha), we hereafter utilize α from model (3a)–(3c) as the representative persister switch rate.

C.

Given that the experimental context [24] for modeling persister switching occurred during population decline, we only employed trials from Belias et al. [9] that exhibited bi-phasic decay. Namely, we did not include trials in which significant bacterial growth was observed at the time scale of successive data points (the time scale in the field study is on the order of 4–16 h for the 1st day and then 24 h thereafter.)

D.

The switch rate from normal to persister cell is on average a monotonic function of some measure of environmental stress.

Based on assumptions A–D above, we applied the model (3a)–(3c) to published pathogen population size and weather data from four replicate trials in Spain, two in California, and one in NY [9]. More specifically, we fit model (3a)–(3c) to the respective population data in order to:

1.

determine values for the maximum switch rate α relative to the produce/bacteria type at the field scale,

2.

describe the correlative relationship between α and weather factors in the respective field trials.

Model fitting procedureIn model (3a)–(3c) above, we supposed dp/dtt = 0  > 0, i.e., we assumed that bacteria experience stress from the change in conditions from culture growth and inoculum suspension preparation to those on the plant surface and therefore, that persister formation increases in the phyllosphere immediately following inoculation. The report that EcO157 persister formation increases as early as 1 h after inoculation into leaf wash water [23], which could be considered as a proxy for the average oligotrophic environment that bacterial cells experience after spray inoculation onto leaves or through irrigation in the field, supports this assumption. To avoid identifiability issues between the initial persister population (widehat {p_0}) and α regarding the model fits above, we assumed that (widehat {p_0})= 1 ((widehat {p_0}) = 0 gives the same results). Thus, the initial persister population at inoculation is at its lowest, an assumption supported by Munther et al. [24], who observed an average fraction of EcO157 persisters of 0.0043% in the inoculum population. This imparts the largest possible switch rate, α, onto the population, corresponding to the largest and hence most conservative food safety risk.Let yk (CFU/100 g of produce) be the average bacteria population measurement at time tk (h) and let Pk,X (CFU/100 g of produce) represent the model prediction (total population) at time tk relative to the parameter vector (X = [ {theta _{n_1} , theta_p , alpha } ]^T). Following Eqs. (3a) and (3b), this means that ({{{{{{{mathrm{P}}}}}}}}_{k,X} = n_1left( {t_k,X} right) + p(t_k,X)). Since the population data spans multiple orders of magnitude, we calculated the residuals as (e_{k,X} = log _{10}y_k – log _{10}P_{k,X}). To determine the optimal model fit (see the appendix for details regarding a priori bounds on parameter ranges), we utilized the fminsearch function in MATLAB (MATLAB 2020b, The MathWorks, Inc., Natick, Massachusetts, United States) to determine the parameter vector X that minimizes the 2-norm of the following function F:$$| | Fleft( X right) | |_2 = left( {mathop {sum }limits_k e_{k,X}^2} right)^{frac{1}{2}}$$Correlation analysisTo provide a statistical foundation from which to relate the switch rate α and measured weather factors, we utilized Spearman and partial Spearman correlation. First, we calculated the Spearman correlation coefficients between α and each of the respective factors: 8-h average of temperature, RH, solar radiation, wind speed post-inoculation, and then we calculated the partial Spearman correlation coefficients for each respective weather factor, while controlling for the other three factors and simultaneously controlling for produce type (using lettuce =1 and spinach =0) (For details regarding why 8-h weather variables were used, see the “model fitting” subsection of the results.) The correlation coefficients were determined using the corr and partialcorr functions in MATLAB 2020b (The MathWorks, Inc., Natick, MA, USA). Considering the significant association of Salmonella α with RH and temperature, we also examined the correlation between α and dew point. Figure 1 presents a logical flow of the statistical analysis. Partial correlations with a P value of less than 0.05 were deemed significant. If the 8-h average of a weather factor exhibited a significant correlation with the switch rate, the 8-h minimum and range of the weather factor were also tested.Fig. 1: Logical flow diagram for statistical analysis.Factors in Step 1: UV (average ultraviolet radiation intensity), RH (average air relative humidity), Wind (average wind speed), and Temp (average air temperature). All weather data used in the statistical analysis were obtained over 8 h post-inoculation of E. coli and Salmonella onto lettuce and spinach leaves in the field.Full size image More

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Countries have yet to agree to protect at least 30% of land, a crucial target proposed in the global biodiversity deal.Credit: Roberto Schmidt/AFP via Getty

A beleaguered global deal to save the environment got a financial boost last week when Germany announced that it was upping its funding for international biodiversity conservation to €1.5 billion (US$1.49 billion) a year — an increase of €0.87 billion — making it the largest national financial pledge yet to save nature. The announcement came at a 20 September meeting in New York City, where political leaders, businesses and conservation and Indigenous-rights groups came together to rally momentum and support ahead of the United Nations biodiversity summit in Montreal, Canada, in December.Conservationists welcomed the extra funding, but warned that other wealthy countries must also reach deeper into their pockets to ensure that nations agree on a new biodiversity agreement, called the Post-2020 Global Biodiversity Framework. Estimates suggest that an additional US$700 billion annually is needed to protect the environment.Concerns over insufficient financing for global biodiversity conservation have stalled negotiations and threaten to derail attempts to finalize a deal in Montreal. The forthcoming summit will be the 15th meeting of the Conference of the Parties (COP15) to the UN’s Convention on Biological Diversity.Announcing the new funds, German Chancellor Olaf Scholz said: “With this contribution, we want to send a strong signal for an ambitious outcome of the biodiversity COP-15.”Claire Blanchard, head of global advocacy at WWF, a conservation group, told Nature that the extra funding “is highly significant” and sends an important signal that rich countries are prepared to step up.But she adds: “More signals of this kind will be needed to create the environment conducive to constructive dialogue in the negotiation room.”Andrew Deutz, a specialist in biodiversity law and finance at the Nature Conservancy, a conservation group in Arlington, Virginia, says he expects further funding announcements to come in the run up to and at the COP15.Other pledgesSeveral key political leaders, including Justin Trudeau, Canada’s prime minister, echoed calls for rich nations to make urgent progress to secure the biodiversity deal. Trudeau urged countries to agree on two crucial targets proposed in the biodiversity framework, both to be met by 2030: to halt and reverse biodiversity loss, and to protect at least 30% of land and seas.The new funding was bolstered by other pledges and developments, including a promise from a partnership of some of the world’s wealthiest private philanthropic foundations and charities to add to the $5 billion they have already committed to conservation, if other countries promise more funds.The partnership — which includes the Bezos Earth Fund, an environmental fund financed by entrepreneur Jeff Bezos — has already spent around $1 billion of its promised financing over the past two years, says Cristián Samper, head of the Wildlife Conservation Society, a not-for-profit group. Samper was speaking on behalf of the partnership at the meeting in New York City.Frans Timmermans, vice-president of the European Commission, reaffirmed that Europe would double its international biodiversity funding to $1.13 billion annually — a promise originally announced in September last year. Timmermans told the meeting that the European Union would set out more details about the funding soon.Funding shortfallAlso at the meeting, a group of four countries comprising Ecuador, Gabon, the Maldives and the United Kingdom launched a joint 10-point plan to bridge the biodiversity finance gap, which is estimated at $700 billion annually.The plan sets out the financial commitments and policy reforms needed to finance biodiversity on the required scale. For example, it encourages wealthy and lower-income nations to allocate new funds for biodiversity and to quickly deliver on their existing financial pledges. It requires donor countries to ensure that funds for overseas development do no harm to biodiversity. And it asks countries to dedicate a portion of their national funding for climate change to activities that also protect and conserve nature.The plan also commits countries to ensuring that public finance is invested in ways that benefit biodiversity, and to reviewing national subsidies and redirecting those that are harmful to nature. It calls on businesses to assess and disclose commercial risks associated with biodiversity decline, and to set quantitative targets to reduce their impact on the natural world. And it encourages multilateral development banks — such as the World Bank in Washington DC — and international financial institutions to ensure that their investments benefit biodiversity, and asks that they report on their biodiversity funding in time for COP-15.So far, 15 countries, including Canada, Germany and Norway, as well as the EU have endorsed the plan.“The plan provides a clear pathway for bridging the global biodiversity finance gap. Its significance lies in the political signal it sends,” says Blanchard.António Guterres, secretary-general of the UN, urged political leaders to “act now and at scale” to secure biodiversity financing and ensure agreement on the framework. “If negotiations continue at their slow pace, we are headed to failure,” he told the meeting. More

Structural characterization of slag samplesThe FTIR spectra of granulated blast furnace slag (Sample 1), waste slag dumped in landfill (Sample 2) and combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3) are presented in Fig. 1.Figure 1FTIR spectra of slag samples.Full size imageBy analysing the spectrum (detailed figure) in the range of 700–1100 cm−1, it can be found that there are obvious absorption peaks in the spectrum of all the slag samples. The granulated blast furnace slag shows the characteristic absorption bands at 3640, 1418, 980, 944, 861, 753 and 710 cm−1. The band at 3640 cm−1 is assigned to the stretching vibration of the hydroxyl group originated from the weakly absorbed water molecules on the slag surface24. The characteristic absorption bands at 1418, 861 and 710 cm−1 are ascribed to the asymmetric stretching mode and bending mode of carbonate group, respectively and the band at 980 cm−1 are attributable to the stretching vibrations of Si–O25. The band at 944 and 752 cm−1 represent the internal vibration of [SiO4]4− and [AlO4]5− tetrahedral and comes from Si (Al)–O-antisymmetric stretching vibration26.The different vibration modes for the sample of waste slag can be observed in the FTIR spectrum. The absorption bands shown are at 1418, 873, 712, 667 and 419 cm−1. The peak at 1418 cm−1 is assigned to the asymmetric stretching mode and bending mode of carbonate group. Calcite phase is confirmed by characteristic peaks at 712 cm−1 (ʋ2 out of plane bending vibration of the CO3−2 ion) and 873 cm−1 peak (ʋ2 split in-plane bending vibrations of the CO3−2 ion27. Calcium aluminate phase is identified by characteristic peak at 419 cm−128. Peak around 667 cm−1 is described as absorption band for different M–O (metal oxide) such as Al–O, Fe–O, Mg–O etc.29.In the case of combination of both 50% granulated blast furnace slag and 50% waste slag dumped in landfill the intensity of absorption peaks is smaller in comparison with Sample 1 and Sample 2 of slag. The characteristic absorption peaks (978 and 753 cm−1) which correspond with characteristic peaks of Sample 1 are shifted compared to the Sample 1, assigned to the stretching vibrations of Si–O and to the Si (Al)–O-antisymmetric stretching vibration, respectively, can provide important evidence of chemical interaction between Sample 1 and Sample 2. The decrease of the intensity of the bands appearing at 875 and 709 cm−1 cans be attributed to overlapping the vibrations of the CO3−2 ion from calcite phase.Figure 2 presents the SEM micrographs of the slag samples (Sample 1–3). One can see the characteristic morphology- the sizes and the forms of the slag samples.Figure 2SEM images of slag samples.Full size imageAt larger magnifications it can be observed that the surface is rough and uneven, and one can notice rounded grain-like rugged formations. The slag samples display aggregated particles with average diameter of a few microns. Also, in these rounded formations it can be seen different morphologies like spheres, rods, boards specific each compound/phase from metallurgical slags.Figure 3 illustrates the EDX elemental analysis of granulated blast furnace slag (Sample 1), waste slag dumped in landfill (Sample 2) and combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3).Figure 3EDX elemental map of slag samples.Full size imageOne can observe that the predominant elements in the examined area are constated in carbon, oxygen, calcium, and iron, confirming the FTIR spectra.Figure 4 shows EDX spectra of slag samples recorded on different selected punctual area, to obtain more information about the elemental composition of specific areas. For all the tested slag samples have similar elements content.Figure 4EDX spectra analysis of slag samples.Full size imageThe selected punctual areas are highlighted thus: the spheric structure are with yellow line and the structure like boards are with green line for all the analysed slag samples. In the case of Sample 1 for both structures the values of chemical elements present are similar and the silicon has a higher value at spheric structure which can be correlated with the presence of silica (SiO2). The higher content of calcium reveals that the Sample 1 is blast furnace slag dominated by calcium and silicon compositions. In the case of slag dumped in landfill (Sample 2) the content of carbon increase for both structures and some chemical elements like titanium, barium, manganese doesn`t appear in EDX spectra and the explanation for this phenomenon is that the slag was dumped in landfill for more than 30 years. One can observe for combination of both 50% granulated blast furnace slag + 50% waste slag dumped in landfill (Sample 3) that the values of all the chemical elements for both spheric and board-like structure are between the first two samples, confirming the FTIR spectra regarding chemical interaction between Sample 1.XRD patterns of the slag samples with the phases identified are shown in Fig. 5. Sample 1 show minor peaks of free CaO and MgO, which may be deleterious and cause reduction in strength. The phases and amorphous contents of the Sample 1 granulated blast furnace slag are broadly consistent with literature30. Sample 3 of slag consists of crystalline phase – Ca2Mg2SiO7, Ca2Fe2AlO5, CaCO3 and CaO as observed by the XRD analysis. In terms of the relations of phase thermal equilibrium, the compounds identified form an isomorphic series of melilites that is specific to basic metallurgical slags.Figure 5X-ray diffraction patterns of slag samples.Full size imageIn Table 1 are presented the values expressed as ppm of chemical element detected in slag samples (Sample 1, 2 and 3).Table 1 XRF analysis of the slag samples.Full size tableThe results show a large quantity of calcium in all three samples of slag. Also, the elements detected such as Fe, Al, Mg and Si are in accordance with XRD spectra.Physical–chemical characterization of soil-slag mixturesThe chemical composition of the major elements that compound the soil, soil- slag and slag samples was determined by XRF. The values expressed as ppm of chemical elements are presented in Table 2. In the case of soil sample the content of the main constituents is iron, titanium, manganese, and potentially toxic elements (PTE) such as arsenic, zinc, copper, and cobalt. For soil-slag 1 with weight ratio soil: slag (1:1) it can be observed the disappearance of the potentially toxic elements (PTE) founded in soil sample and the decrease of concentration value of zinc. When the weight ratio of slag increases at 3 (soil-slag 2 sample) the values of main component increased in accordance with values of slag sample, but in the case of soil-slag 3 sample where the weight ratio of soil is bigger (3) it can be observed the cobalt presence. Based on these XRF results we can say that take place an elimination of potentially toxic elements in contaminated soil by applying slag in a bigger proportion.Table 2 XRF analysis of the soil-slag samples.Full size tableWith the aid of a pH meter, CONSORT C 533 the important parameters of soil and slag solutions were measured as: the pH, conductivity, and the salinity, as shown in Table 3. The data presented in Table 3 suggest that the soil sampled has the pH = 5.2 corresponding to a medium acid soil, which does not sustain a high fertility and is not able to offer proper conditions for crops. Also, the pH of soil has important influence on soil fertility, decreases the availability of essential elements and the activity of soil microorganisms which can determine calcium and magnesium deficiency in plants and decreases phosphorous availability. The pH value of slag solution (12.5) corresponds as strongly basic character which is beneficial in amelioration process of acidic soils and the presence of this type of slag sustain the improving of soil characteristics, too. For the soil-slag samples the pH value increase with the increasing of the weight ratio of slag and the mixtures soil-slag obtained can be framed into the category of weakly alkaline soils.Table 3 The physical–chemical characteristics of soil and slag solutions.Full size tableThe data given in Table 4 show that the humidity of soil is bigger and decreases in soil-slag samples with adding of slag content. The values of total soil-slag porosity are between 40 and 50% and depends on the density and apparent density of the soil being influenced by the mineralogical composition, the content of organic matter and the degree of compaction and loosening of the soil, the crystalline structure of soil minerals.Table 4 The physical–chemical characteristics of soil-slag samples.Full size tableConsidering the structural and morphological characterization of the investigated slag samples we propose a recipe of blast furnace slag and of waste slag dumped in landfill in accordance with the waste directive 2008/98/EC regarding the strategic goal of EU to a complete elimination of the disposal of wastes. The slag dump of Steel Plant of Galati has an enormous quantity of unused waste slag which may be mixed with granulated blast furnace slag, to save the natural resources used as raw materials in the metallurgical technological process.The presence of Ca2+ in the composition of the slag can maintain high alkalinity in the soil for a long time in the natural environment. The alkaline pH of the soil may contribute to a decrease the available concentration of heavy metals by reducing metal mobility and bonding metals into more stable fractions. One of the objectives of this research is improving the quality of the environment by using the mixture between two different slags on agricultural lands and reintroducing them in the agricultural centre, especially in acid soils. Acidic soils are characterized by an acidic pH that has spread in recent years due to excessive fertilizers or far too aggressive work31. The production is significantly influenced, and the treatment of acid soils is usually done using a series of natural materials (lime, dolomite), the consumption being approx. 20 t/hectare depending on the acidity of the soil and the nature of the plants grown on the respective surfaces.Our research consists in improving the characteristics and qualities of the acidic soils and helping to reintroduce it into the agricultural circuit by transforming a waste into a new material friendly-environmental, the mixture of blast furnace slag and waste slag dumped in landfill. More