Microbiological air sampling was conducted in triplicate for each season (dry and rainy) at 5 monitoring points (Figure 1). At each established point, three samplings were carried out in the morning from 7:00 to 9:00 a.m., in the afternoon from 1:00 to 3:00 p.m., and in the evening from 6:00 to 8:00 p.m.

In Figure 1, the coordinates of sampling points P1-P5 can be observed. These points were carefully selected based on wind direction and speed obtained from a compass rose (Figure 2) and the proximity to the San Lucas River, where untreated wastewater from the old treatment plant is discharged. Using this information, the sampling points were located at different distances from the plant to capture the dispersion of contaminants representatively. Specifically, P1 is situated before the plant, at 0.5 km P2 and P3 are 0.05 km and 0.1 km far from the plant, respectively. P4 is at 0.34 km, and P5 is at 1.0 km from the plant, both near the urban area.

For monitoring, a height of 2 m above ground level was chosen to represent human exposure in urban areas. This choice is based on the guidelines of the National Air Quality Monitoring Protocol of Peru (MINAM, 2019), which recommends a height between 1.5 and 3 m to reflect the human breathing zone. Similarly, the Environmental Protection Agency (2017) suggest heights close to 2 m in urban settings to reflect people’s exposure to air contaminants.

Sampling was conducted at a flow rate of 40 L/min for 5 min using the HYCON RCS Standard microbiological sampler by Merck Millipore and tryptic soy agar strips. Temperature and humidity were measured with a Boeco SH-110 thermohygrometer by Boeco Germany. Wind direction and speed were measured with an Extech AN200 anemometer from Extech Instruments Corporation. Using these data, a compass rose was created (Figure 2) to define wind variation in the study area for locating the monitoring points (Figure 1).

The samples were preserved and transported to the laboratory of the Universidad Privada del Norte, where they were incubated at 37°C for 24 h. After the incubation period, colony counts were performed for each tryptic soy agar strip, which contains 34 grids. Then, the conversion from CFU to CFU/m³ was carried out using the conversion factor described in Equation 1. CFU m 3 = CFU ∗ 40   L min ∗ 1   m 3 1000   L (1)

The results were compared to the OSHA (Occupational Safety and Health Administration) indoor air quality standard since there is no official international standard for outdoor microbiological air quality that establishes values for total microorganism counts. The results for bacteria and fungi concentrations were compared to two Polish standards, PN-89/Z-04111/02 and PN-89/Z-04111/03, respectively. Additionally, for data analysis, the ANOVA test was used to determine if there were significant differences in microorganism concentrations concerning seasons, time of day, and sampling points. Tukey’s Honestly Significant Difference (HSD) test was also employed to identify statistically significant differences between points and monitoring times. Finally, Pearson’s Correlation was used to compare the microorganism values with the meteorological variables recorded in the field.

The microbial morphology of the colonies that grew on the stripes was analyzed by performing Gram staining for bacteria and lactophenol blue staining for fungi, using a ZEISS Primostar compound microscope manufactured by Carl Zeiss AG. This facilitated the identification of the species present. After the isolation and morphological observation of the colonies, bacterial strains were sent to a commercial clinical laboratory, where standard biochemical tests were performed to identify genera. The data analysis was carried out using IBM SPSS Statistics version 26 to create tables for the identified genera of bacteria and fungi.

A survey was designed with closed-ended and multiple-choice questions, based on the Likert scale (Likert, 1932), which assigns values to the incidence of health problems: very frequently (5), frequently (4), occasionally (3), rarely (2), and never (1).

The questionnaire was then administered in person to 130 individuals living in the most populated blocks within the area influenced by the former WWTP. Questions that included the Likert scale were identified, and the corresponding scores were assigned to proceed with summing the results. The sum of these scores established the levels of incidence of health problems related to the microbiological quality of the air (Supplementary Table S1). Additionally, Cronbach’s alpha statistical test was applied to the questions following the Likert scale to indicate the degree of internal consistency among the scale’s items (Casas et al., 2003), using IBM SPSS Statistics version 26.

The results presented in Table 1 show elevated levels of bioaerosol concentrations in the area of influence of the former WWTP in Cajamarca. The highest concentrations were 4560 CFU/m³ at the monitoring point P2 during the afternoon and 4115 CFU/m³ at the point P4 during the evening. Sixteen measurements exceeded the limits established by OSHA (2001) for determining air quality concerning the total count of microorganisms (Figure 3). Ten measurements exceeded the PN-89/Z-04111/02 standard for bacteria (Figure 4). This standard classifies air as moderately contaminated when concentrations range between 1,000 CFU/m³ and 3,000 CFU/m³. On the other hand, although fungal levels were elevated, they did not exceed the 5,000 CFU/m³ limit established by the PN-89/Z-04111/03 standard (Figure 5).

These results underscore the impact of the former WWTP on local air quality, especially in the absence of an adequate wastewater treatment system. Staszowska (2022) conducted a study in a municipal WWTP in Poland, where the highest outdoor bacterial concentration was 3617 CFU/m³. Our study also revealed higher fungal concentrations compared to similar studies. In an Indonesian WWTP, the highest recorded fungal concentration was 1955 CFU/m³ (Kristanto and Rosana, 2017), while a study in Brazil recorded a maximum concentration of 330 CFU/m³ (Papais et al., 2022). These differences could be related to the characteristics of the infrastructure and the specific environmental conditions of Cajamarca, which favor the accumulation of bioaerosols in the air (Castro et al., 2021).

Water quality studies have determined high levels of fecal coliforms such as Escherichia coli and Enterococcus in the San Lucas River in areas near our monitoring points (García, 2014; Escalante, 2018; Autoridad Nacional del Agua del Perú, (2019). For instance, up to 490,000 MPN/100 mL of fecal coliforms and 110,000 MPN/100 mL of Escherichia coli were found in the river (ANA, 2020). Additionally, studies at WWTPs in various locations have demonstrated that wastewater management releases pathogenic microorganisms into the air (Niazi et al., 2015; Kermani et al., 2016; Kristanto and Rosana, 2017; Małecka-Adamowicz et al., 2017; Pasmionka, 2019; Staszowska, 2022). Therefore, the high concentration of bioaerosols in the study area and surrounding zones is due to the presence, discharge, and runoff of wastewater, such as that generated by the former WWTP, into the San Lucas River. This exposes the nearby population to a higher risk of inhaling potentially pathogenic bioaerosols.

Table 4 presents the genera of microorganisms found in the air at the five sampling points. Eight bacterial genera were identified: Micrococcus, Rhodococcus, Bacillus, Staphylococcus, Streptococcus, Enterococcus, Escherichia, and Proteus. These bacteria are common in natural and urban environments, and some are known for their pathogenic potential, making their monitoring crucial in urban settings (Chen et al., 2020). Regarding fungi, five genera were identified: Aspergillus, Penicillium, Rhodotorula, Mucor, and Rhizopus. These microorganisms are particularly relevant due to their ability to produce mycotoxins and cause infections, especially in immunocompromised individuals (Brown et al., 2021). The presence of these pathogenic microorganisms in inhabited areas near the former WWTP highlights the potential risk for residents, as prolonged exposure to these bioaerosols could lead to health issues, particularly in vulnerable groups (Jahne et al., 2015).

Regarding bacteria, a study conducted at a wastewater treatment plant (WWTP) in Bydgoszcz, Poland, revealed that Staphylococcus was the most abundant bacterial genus at all monitoring points throughout the WWTP (Małecka-Adamowicz et al., 2017). As for fungi, our results align with those found at a WWTP in Tehran, Iran (Niazi et al., 2015). Here, genera known for thriving in unfavorable environments due to their metabolic capabilities, such as Cladosporium spp., Penicillium spp., Aspergillus spp., and Alternaria spp., were identified. Similarly, in the Gulf of Gdansk, Poland, fungi of the genera Penicillium and Aspergillus were identified in the wastewater discharge area, reinforcing the prevalence of these genera in contaminated aquatic environments (Michalska et al., 2021).

The statistical analysis of the information obtained from the survey was conducted using IBM SPSS version 26, where Cronbach’s alpha was applied to evaluate the reliability of the scale used. The obtained value was 0.823, which is within the acceptable range, as the minimum acceptable value is 0.70 and the maximum expected value is 0.90 (Oviedo and Campo, 2022). This result indicates that the scale used is reliable and has good internal consistency.

Figure 8 presents the impact of microorganisms on the health of the population in the area of influence of the former WWTP. Of the total respondents, 34% reported a low level of health problems, followed by 29% with a normal level. 18% indicated a high level of incidence, and 15% reported a very low level. Only 4% showed a very high level of incidence. These results may be influenced by various factors, such as age, location, and length of residence, which affect the population’s vulnerability to the microorganisms present in the air (Vargas, 2005). Herrera et al. (2009) suggested that exposure to microbiological contamination does not necessarily translate into high levels of health incidence. The lack of health knowledge and constant exposure to this environment influence the subjective perception of the respondents. This causes individuals to not fully identify the effects of these microorganisms on their wellbeing.

Table 5 presents the relationship between the symptoms reported by the population and the microorganisms identified in the microbiological sampling. 77% of the reported symptoms were nasal problems, such as sneezing, rhinitis, congestion, and dryness, which are commonly associated with bacteria such as Bacillus sp., B. subtilis, Rhodococcus sp., Micrococcus sp. (Chen et al., 2020) and fungi such as Aspergillus sp., Mucor sp., and Rhizopus sp. (Brown et al., 2021). Suaza and Valoyes (2019) in Antioquia, Spain, established a close relationship between air pollution and respiratory problems in the population, particularly in children and the elderly. Additionally, particulate matter has been identified as a factor that influences the development and persistence of bioaerosols. That is why it is necessary to further explore the interaction between these variables and their potential long-term health effects (Liu et al., 2020).