4. Research report

4.1 Aim:
Since December 2019, the global COVID-19 pandemic has significantly impacted theutilization of pharmaceuticals and personal care products (PPCPs), leading to potential
adverse effects on aquatic environments. This study aims to collect and analyze waste
and surface water samples from Sri Lanka during the COVID-19 period. The researchbuilds upon prior environmental monitoring of PPCPs conducted before the pandemic. A
comparative analysis of data collected before and during COVID-19 will be undertaken
to comprehend potential environmental consequences.

4.2 Objectives:
The following studies will be conducted to assess the influence of the COVID-19
pandemic on surface and wastewater in Sri Lanka:(1) Examination of the presence of PPCPs in diverse aquatic environments.
(2) Assessment of the detrimental ecological impacts of PPCPs on the most vulnerable
aquatic species.
(3) Investigation into the association between the occurrence of antimicrobials and thedevelopment of drug-resistant bacteria and resistance genes.

4.3 Materials and Methods
4.3.1 Sample collection:
In 2022, amidst the surge of the Omicron variant of COVID-19, a total of 91 samples
were collected across five seasons (January, February, April, July, and December) fromdiverse sources. These sources included eight hospitals, four surface water samplingsites (lake, canal, and river), and a wastewater treatment plant (WWTP) in the Kandy
area of Sri Lanka (Figure and Table).
4.3.2 Extraction and detection:
In this study, approximately 100 selected pharmaceuticals, including 37 antibiotics, weretargeted in water samples. The analytical method employed modifications of previous
procedures (Guruge et al., 2019; Tanoue et al., 2015). Briefly, samples underwent
2 / 5

filtration using glass-fiber filters to eliminate suspended solids. A 20 mL aliquot of thefiltrate was acidified with formic acid and spiked with internal standards. The sample
solution went through Oasis HLB and Oasis MCX cartridges, preceded by specificpreconditioning steps. After loading and washing, antimicrobial agents were eluted fromthe cartridges, combined, evaporated, and finally diluted for identification andquantification using LC–MS/MS. To minimize adsorption, containers and vials made ofPP or PE were exclusively utilized, and acetonitrile replaced methanol in the analysis ofbeta-lactam antibiotics.

4.4 Results
4.4.1 Drug usage pattern in hospitals
According to epidemiological data on drug usage in the Kandy district, information
gathered from the Regional Medical Supply Department (RMSD) in the Kandy district
indicates a priority usage of certain drugs. The penicillin family drugs, specificallyamoxicillin, cloxacillin, and Co-amoxiclav, were identified as the most frequently used.
In the category of cephalosporins, cefalexin, cefuroxime, and ceftriaxone were prioritized
as 1st, 2nd, and 3rd-generation options, respectively. Doxycycline emerged as theprimary tetracycline drug. Among macrolides, clarithromycin, erythromycin, andazithromycin were the most commonly utilized, while Co-trimoxazole (a mixture ofsulfamethoxazole and trimethoprim) was the preferred choice for sulfonamides.
Ciprofloxacin stood out as the predominant fluoroquinolone. In the realm of antiparasitic
and antifungal drugs, metronidazole, mebendazole, and nystatin were given priority. For
psychiatricdrugs,priority

wasaccorded

tocarbamazepine,phenytoin,andchlorpromazine. In the cardiac drugs category, losartan, atorvastatin, and diltiazem wereamong the prioritized options. Non-steroidal anti-inflammatory drugs (NSAIDs) wererepresented by ibuprofen and diclofenac, which received high priority. At the COVID-19
Intermediate

CareCenter(H-8),

patients

werepredominantlyadministeredazithromycin, amoxicillin and ciprofloxacin antibiotics. Additionally, azithromycin wasconsistently used for treating all COVID-19 patients in other hospitals, suggesting its
potential as a drug-marker for tracing COVID-19 related drugs in wastewater andsurface waters.
4.4.2

Occurrence of antimicrobials (AMs)

Eleven drug classes of antimicrobials (AMs) comprising 37 individual drugs were chosen
for this analysis. The recovery criteria were set as follows: absolute recovery exceeding
30%, an acceptable range corrected with internal standards falling between 70 – 120%,
and a variation of less than 15%. For the initial analysis, two sample sets were examined.
The first set, collected in January 2022, coincided with the rise of the Omicron variantof COVID-19, while the second set, collected in December 2022, was during the period
when COVID-19 surveillance was not conducted in Sri Lanka. Samples were selectedfrom the largest hospital (H-1), H-8, WWTP inlet and outlet, adjacent river, canal, and3 / 5

lake.
A total of 22 compounds were identified in at least one sample; nevertheless, only 16 ofthese compounds have been chosen for discussion, as indicated in the table. The highest
AM detected in our analysis was metronidazole (791000 ng/L), an antiparasitic drug
prioritized for use, identified in the sample associated with COVID-19 patients from H1. Additionally, ciprofloxacin and azithromycin were also present in high concentrationsin the same sample collected during the surge of COVID-19 cases. A similar trend wasobserved in samples from H-8, where these two drugs, along with clarithromycin,
exhibited concentrations several magnitudes higher than those in non-COVID samples.
Notably, the concentration of azithromycin was 2620-fold higher. This data suggests thatazithromycin could serve as a significant marker for tracing COVID-related drugs inenvironmental waters.
Our analysis also identified two beta-lactam drugs, cefuroxime and piperacillin, detectedin samples from the WWTP outlet, river, and connected canal, raising the need forfurther investigation into why they were not present in untreated hospital wastewater.
Among other antimicrobials, doxycycline, sulfamethoxazole, and trimethoprim werefound in high concentrations, aligning with their prioritization as important AMs in
hospital settings.
Even though not all samples have been analyzed yet, the initial estimate of the removal
efficiency of PPCPs in the WWTP appears satisfactory, ranging from 58% to 99% forsulfonamides,trimethoprim,macrolides,lincosamides,fluoroquinolones,andtetracyclines.

5. Future challenges:
Currently, remaining target PPCPs have been analyzing in rest of samples. Once all
samples are analyzed, the 2nd and 3rd objectives will be carried out to emphasizes theecological impacts of detected PPCPs together with their association for antimicrobials
and occurrence of drug-resistant bacteria and resistance genes. The additional samples
which were collected in December 2023 from the same locations will also be analyzed
under the Lamer 2023 research program.

6. Acknowledgment:This study was conducted with the support of Grants-in-Aid for Scientific Research Fundfor the Promotion of International Collaborative Research (B: 21KK0188) and LaMer,
Ehime University, Japan. We extend our gratitude to our collaborators at the Universityof Peradeniya, Joint Research and Demonstration Centre (JRDC), Ministry of Water
Supply, Ministry of Health (Hospital Staff in the Kandy area), Kandy City Wastewater
Management Project, and the National Water Supply and Drainage Board of Sri Lanka
for their valuable contributions and support.

4 / 5Figure. Sample locations
H-1: Largest Hospital[samples were collected from four sites]
H-8: COVID-19 Intermediate Care Center[samples were collected from two sites]

Table. Concentration of antimicrobials detected in aquatic samples from Kandy area-2022
H-1-1
Sulfapyridine

H-1-2

H-1-4

H-1-3 (covid+)

H-8 (covid+)

H-8

WWTP-Inlet

WWTP-Outlet

River - Before

River- AfterCanalLake

January

DecemberJanuary

DecemberJanuary

January

DecemberJanuaryJanuary

DecemberJanuary

DecemberJanuary

DecemberJanuary

DecemberJanuary

DecemberJanuary

DecemberJanuary

December

12300

1191875

1285<MDL<MDL78<MDL44735

2165

1025

825

278

6231498<MDL<MDL

Sulfamethoxazole

64000

525

455

235

2390

10136

<MDL

<MDL6550

1770

940

560

490

412<MDL

205<MDL2

Trimethoprim

13900

310

313

221

11700

2715<MDL

<MDL

279

695

220

107

96

1.3

<MDL

0.5

<MDL

151<MDL<MDL

Lincomycin

174

103

231816<MDL<MDL<MDL<MDL<MDL

11841<MDL<MDL<MDL<MDL

0.30.1<MDL0.1

Clindamycin

50500

13000

780

2310

7100

26880.42101130

5309403611110.313811

Clarithromycin

118568520200

800

131862520<MDL292457

9530.3

20.04

0.1

0.25

0.01

190.02Azithromycin

126

1500

53537530

431411300

29353201<MDL4<MDL

1<MDL

1<MDL

0.5

190.4

Vancomycin

9632298

1400

14520

<MDL

<MDL

<MDL

<MDL

221

1211791185<MDL<MDL<MDL63<MDL<MDL<MDL

Levofloxacin/ofloxacin

29515300

8116020

14550

560

111

3107

89

395

413

80

48

31

<MDL

<MDL

1<MDL

2<MDL

Ciprofloxacin

5246

2961

2906

7246

39096

3961

5246

94065

1980

2341

4446

120

113

89

<MDL

4<MDL

<MDL

18

<MDL

Doxycyline

234476

108

104

2350

105

220.4

20

1183

170

15

290.4

2<MDL

0.1

0.9

1.0

10.2

Fluconazole

219

149

46

67

4

398

10

1.5

1.3

6.6

138

55

173

63

4.9

2.8

2.3

1.6

25

4

3.2

3.9

Ketoconazole

1647

<MDL

59

688392

1850

90

2

283719131554

41155219Metronidazole

118880193322200

79100013900

808

8<MDL<MDL317029930854822184621527011Cefuroxime<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL12132469<MDL

38<MDL<MDL

Piperacillin<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL<MDL

83286

34<MDL85<MDL<MDL5 / 5